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[] | Poster | [] | Vertical federated learning (VFL), where each participating client holds a subset of data features, has found numerous applications in finance, healthcare, and IoT systems. However, adversarial attacks, particularly through the injection of adversarial examples (AEs), pose serious challenges to the security of VFL models. In this paper, we investigate such vulnerabilities through developing a novel attack to disrupt the VFL inference process, under a practical scenario where the adversary is able to *adaptively corrupt a subset of clients*. We formulate the problem of finding optimal attack strategies as an online optimization problem, which is decomposed into an inner problem of adversarial example generation (AEG) and an outer problem of corruption pattern selection (CPS). Specifically, we establish the equivalence between the formulated CPS problem and a multi-armed bandit (MAB) problem, and propose the Thompson sampling with Empirical maximum reward (E-TS) algorithm for the adversary to efficiently identify the optimal subset of clients for corruption. The key idea of E-TS is to introduce an estimation of the expected maximum reward for each arm, which helps to specify a small set of *competitive arms*, on which the exploration for the optimal arm is performed. This significantly reduces the exploration space, which otherwise can quickly become prohibitively large as the number of clients increases. We analytically characterize the regret bound of E-TS, and empirically demonstrate its capability of efficiently revealing the optimal corruption pattern with the highest attack success rate, under various datasets of popular VFL tasks. | [] | [] | Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit | [
"Duanyi YAO",
"Songze Li",
"Ye XUE",
"Jin Liu"
] | 17,906 | https://openreview.net/forum?id=m52uU0dVbH |
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[] | Poster | [] | Crystals are the foundation of numerous scientific and industrial applications. While various learning-based approaches have been proposed for crystal generation, existing methods neglect the spacegroup constraint which is crucial in describing the geometry of crystals and closely relevant to many desirable properties. However, considering spacegroup constraint is challenging owing to its diverse and nontrivial forms. In this paper, we reduce the spacegroup constraint into an equivalent formulation that is more tractable to be handcrafted into the generation process. In particular, we translate the spacegroup constraint into two cases: the basis constraint of the invariant exponential space of the lattice matrix and the Wyckoff position constraint of the fractional coordinates. Upon the derived constraints, we then propose DiffCSP++, a novel diffusion model that has enhanced a previous work DiffCSP by further taking spacegroup constraint into account. Experiments on several popular datasets verify the benefit of the involvement of the spacegroup constraint, and show that our DiffCSP++ achieves the best or comparable performance on crystal structure prediction and ab initio crystal generation. | [] | [] | Space Group Constrained Crystal Generation | [
"Rui Jiao",
"Wenbing Huang",
"Yu Liu",
"Deli Zhao",
"Yang Liu"
] | 2402.03992 | 18,009 | https://openreview.net/forum?id=jkvZ7v4OmP |
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[] | Poster | [] | Machine Learning (ML) models have been utilized for malware detection for over two decades. Consequently, this ignited an ongoing arms race between malware authors and antivirus systems, compelling researchers to propose defenses for malware-detection models against evasion attacks. However, most if not all existing defenses against evasion attacks suffer from sizable performance degradation and/or can defend against only specific attacks, which makes them less practical in real-world settings. In this work, we develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the *de-randomized smoothing* technique for the domain of malware detection. Specifically, we propose a *window ablation* scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables. After showing how DRSM is theoretically robust against attacks with contiguous adversarial bytes, we verify its performance and certified robustness experimentally, where we observe only marginal accuracy drops as the cost of robustness. To our knowledge, we are the first to offer certified robustness in the realm of static detection of malware executables. More surprisingly, through evaluating DRSM against $9$ empirical attacks of different types, we observe that the proposed defense is empirically robust to some extent against a diverse set of attacks, some of which even fall out of the scope of its original threat model. In addition, we collected $15.5K$ recent benign raw executables from diverse sources, which will be made public as a dataset called PACE (Publicly Accessible Collection(s) of Executables) to alleviate the scarcity of publicly available benign datasets for studying malware detection and provide future research with more representative data of the time. | [] | [] | DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness | [
"Shoumik Saha",
"Wenxiao Wang",
"Yigitcan Kaya",
"Soheil Feizi",
"Tudor Dumitras"
] | 2303.13372 | 17,905 | https://openreview.net/forum?id=m7aPLHwsLr |
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[] | Poster | [
"https://github.com/deeplearning-wisc/sal"
] | Using unlabeled data to regularize the machine learning models has demonstrated promise for improving safety and reliability in detecting out-of-distribution (OOD) data. Harnessing the power of unlabeled in-the-wild data is non-trivial due to the heterogeneity of both in-distribution (ID) and OOD data. This lack of a clean set of OOD samples poses significant challenges in learning an optimal OOD classifier. Currently, there is a lack of research on formally understanding how unlabeled data helps OOD detection. This paper bridges the gap by introducing a new learning framework SAL (Separate And Learn) that offers both strong theoretical guarantees and empirical effectiveness. The framework separates candidate outliers from the unlabeled data and then trains an OOD classifier using the candidate outliers and the labeled ID data. Theoretically, we provide rigorous error bounds from the lens of separability and learnability, formally justifying the two components in our algorithm. Our theory shows that SAL can separate the candidate outliers with small error rates, which leads to a generalization guarantee for the learned OOD classifier. Empirically, SAL achieves state-of-the-art performance on common benchmarks, reinforcing our theoretical insights. Code is publicly available at https://github.com/deeplearning-wisc/sal. | [] | [] | How Does Unlabeled Data Provably Help Out-of-Distribution Detection? | [
"Xuefeng Du",
"Zhen Fang",
"Ilias Diakonikolas",
"Yixuan Li"
] | 2402.03502 | 18,008 | https://openreview.net/forum?id=jlEjB8MVGa |
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[] | Poster | [] | Daily internet communication relies heavily on tree-structured graphs, embodied by popular data formats such as XML and JSON. However, many recent generative (probabilistic) models utilize neural networks to learn a probability distribution over undirected cyclic graphs. This assumption of a generic graph structure brings various computational challenges, and, more importantly, the presence of non-linearities in neural networks does not permit tractable probabilistic inference. We address these problems by proposing sum-product-set networks, an extension of probabilistic circuits from unstructured tensor data to tree-structured graph data. To this end, we use random finite sets to reflect a variable number of nodes and edges in the graph and to allow for exact and efficient inference. We demonstrate that our tractable model performs comparably to various intractable models based on neural networks. | [] | [] | Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs | [
"Milan Papez",
"Martin Rektoris",
"Vaclav Smidl",
"Tomáš Pevný"
] | 17,901 | https://openreview.net/forum?id=mF3cTns4pe |
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[] | Poster | [] | It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model. | [] | [] | Language Modeling Is Compression | [
"Gregoire Deletang",
"Anian Ruoss",
"Paul-Ambroise Duquenne",
"Elliot Catt",
"Tim Genewein",
"Christopher Mattern",
"Jordi Grau-Moya",
"Li Kevin Wenliang",
"Matthew Aitchison",
"Laurent Orseau",
"Marcus Hutter",
"Joel Veness"
] | 2309.10668 | 17,997 | https://openreview.net/forum?id=jznbgiynus |
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[] | Poster | [] | Recent work has showcased the ability of large-scale language models (LLMs) to embody diverse personas in their responses, exemplified by prompts like "_You are Julius Caesar. Compose a rap about Climate Change._" However, it remains unclear how these persona assignments indirectly influence LLMs' core capabilities. We present the first extensive study of this in the context of LLMs' ability to perform basic reasoning. Our study encompasses 16 personas spanning 5 diverse groups (race, gender, religion, disability, and political affiliation), across 24 reasoning datasets in diverse domains such as mathematics, history, law, ethics, and more. Our findings unveil that while LLMs, such as ChatGPT, overtly reject stereotypes when explicitly asked ("_Are Black people inept at mathematics?_"), they tend to manifest implicit stereotypical and often erroneous presumptions when prompted to take on a persona (e.g., abstentions in rationales such as "_As a Black person, I am unable to answer this question as it requires math knowledge_"). This results in substantial disparities in reasoning performance among personas. This inherent 'deep' bias permeates extensively, leading to a statistically significant performance drop in over 95\% of our datasets for certain personas, with as much as 70\% relative drop in accuracy on select datasets. Beyond explicit abstentions, these models also have implicitly biased reasoning not evident in their responses. We find that simple prompt-based mitigation approaches have minimal impact. Our findings serve as a cautionary tale that the practice of assigning personas to LLMs---a trend on the rise---can surface their deep-rooted biases and have unforeseeable and detrimental side-effects. | [] | [] | Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs | [
"Shashank Gupta",
"Vaishnavi Shrivastava",
"Ameet Deshpande",
"Ashwin Kalyan",
"Peter Clark",
"Ashish Sabharwal",
"Tushar Khot"
] | 2311.04892 | 17,986 | https://openreview.net/forum?id=kGteeZ18Ir |
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[] | Poster | [] | Neural Controlled Differential Equations (NCDE) are a state-of-the-art tool for supervised learning with irregularly sampled time series (Kidger 2020). However, no theoretical analysis of their performance has been provided yet, and it remains unclear in particular how the roughness of the sampling affects their predictions. By merging the rich theory of controlled differential equations (CDE) and Lipschitz-based measures of the complexity of deep neural nets, we take a first step towards the theoretical understanding of NCDE. Our first result is a sampling-dependant generalization bound for this class of predictors. In a second time, we leverage the continuity of the flow of CDEs to provide a detailed analysis of both the sampling-induced bias and the approximation bias. Regarding this last result, we show how classical approximation results on neural nets may transfer to NCDE. Our theoretical results are validated through a series of experiments, for which the code is available at REDACTED. | [] | [] | On the Generalization and Approximation Capacities of Neural Controlled Differential Equations | [
"Linus Bleistein",
"Agathe Guilloux"
] | 2305.16791 | 17,985 | https://openreview.net/forum?id=kILAd8RdzA |
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[] | Spotlight Poster | [] | The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce \foldflow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3\mathrm{D}$ rigid motions---i.e. the group $\mathrm{SE}(3)$---enabling accurate modeling of protein backbones. We first introduce FoldFlow-Base, a simulation-free approach to learning deterministic continuous-time dynamics and matching invariant target distributions on $\mathrm{SE}(3)$. We next accelerate training by incorporating Riemannian optimal transport to create FoldFlow-OT, leading to the construction of both more simple and stable flows. Finally, we design FoldFlow-SFM, coupling both Riemannian OT and simulation-free training to learn stochastic continuous-time dynamics over $\mathrm{SE}(3)$. Our family of FoldFlow, generative models offers several key advantages over previous approaches to the generative modeling of proteins: they are more stable and faster to train than diffusion-based approaches, and our models enjoy the ability to map any invariant source distribution to any invariant target distribution over $\mathrm{SE}(3)$. Empirically, we validate our FoldFlow, models on protein backbone generation of up to $300$ amino acids leading to high-quality designable, diverse, and novel samples. | [] | [] | SE(3)-Stochastic Flow Matching for Protein Backbone Generation | [
"Joey Bose",
"Tara Akhound-Sadegh",
"Guillaume Huguet",
"Kilian FATRAS",
"Jarrid Rector-Brooks",
"Cheng-Hao Liu",
"Andrei Cristian Nica",
"Maksym Korablyov",
"Michael M. Bronstein",
"Alexander Tong"
] | 17,980 | https://openreview.net/forum?id=kJFIH23hXb |
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[] | Poster | [] | Theoretical and empirical comparisons have been made to assess the expressive power and performance of invariant and equivariant GNNs. However, there is currently no theoretical result comparing the expressive power of $k$-hop invariant GNNs and equivariant GNNs. Additionally, little is understood about whether the performance of equivariant GNNs, employing steerable features up to type-$L$, increases as $L$ grows -- especially when the feature dimension is held constant. In this study, we introduce a key lemma that allows us to analyze steerable features by examining their corresponding invariant features. The lemma facilitates us in understanding the limitations of $k$-hop invariant GNNs, which fail to capture the global geometric structure due to the loss of geometric information between local structures. Furthermore, we investigate the invariant features associated with different types of steerable features and demonstrate that the expressiveness of steerable features is primarily determined by their dimension -- independent of their irreducible decomposition. This suggests that when the feature dimension is constant, increasing $L$ does not lead to essentially improved performance in equivariant GNNs employing steerable features up to type-$L$. We substantiate our theoretical insights with numerical evidence. | [] | [] | Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks | [
"Shih-Hsin Wang",
"Yung-Chang Hsu",
"Justin Baker",
"Andrea L. Bertozzi",
"Jack Xin",
"Bao Wang"
] | 17,900 | https://openreview.net/forum?id=mGHJAyR8w0 |
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[] | Spotlight Poster | [] | Current vision-language generative models rely on expansive corpora of $\textit{paired}$ image-text data to attain optimal performance and generalization capabilities. However, automatically collecting such data (e.g. via large-scale web scraping) leads to low quality and poor image-text correlation, while human annotation is more accurate but requires significant manual effort and expense. We introduce $\textbf{ITIT}$ ($\textbf{I}$n$\textbf{T}$egrating $\textbf{I}$mage $\textbf{T}$ext): an innovative training paradigm grounded in the concept of cycle consistency which allows vision-language training on $\textit{unpaired}$ image and text data. ITIT is comprised of a joint image-text encoder with disjoint image and text decoders that enable bidirectional image-to-text and text-to-image generation in a single framework. During training, ITIT leverages a small set of paired image-text data to ensure its output matches the input reasonably well in both directions. Simultaneously, the model is also trained on much larger datasets containing only images or texts. This is achieved by enforcing cycle consistency between the original unpaired samples and the cycle-generated counterparts. For instance, it generates a caption for a given input image and then uses the caption to create an output image, and enforces similarity between the input and output images. Our experiments show that ITIT with unpaired datasets exhibits similar scaling behavior as using high-quality paired data. We demonstrate image generation and captioning performance on par with state-of-the-art text-to-image and image-to-text models with orders of magnitude fewer (only 3M) paired image-text data. | [] | [] | Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency | [
"Tianhong Li",
"Sangnie Bhardwaj",
"Yonglong Tian",
"Han Zhang",
"Jarred Barber",
"Dina Katabi",
"Guillaume Lajoie",
"Huiwen Chang",
"Dilip Krishnan"
] | 2310.03734 | 17,977 | https://openreview.net/forum?id=kNjrhD67LP |
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[] | Poster | [] | We propose and theoretically analyze an approach for planning with an approximate model in reinforcement learning that can reduce the adverse impact of model error. If the model is accurate enough, it accelerates the convergence to the true value function too. One of its key components is the MaxEnt Model Correction (MoCo) procedure that corrects the model’s next-state distributions based on a Maximum Entropy density estimation formulation. Based on MoCo, we introduce the Model Correcting Value Iteration (MoCoVI) algorithm, and its sampled-based variant MoCoDyna. We show that MoCoVI and MoCoDyna’s convergence can be much faster than the conventional model-free algorithms. Unlike traditional model-based algorithms, MoCoVI and MoCoDyna effectively utilize an approximate model and still converge to the correct value function. | [] | [] | Maximum Entropy Model Correction in Reinforcement Learning | [
"Amin Rakhsha",
"Mete Kemertas",
"Mohammad Ghavamzadeh",
"Amir-massoud Farahmand"
] | 2311.17855 | 17,976 | https://openreview.net/forum?id=kNpSUN0uCc |
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[] | Poster | [
"https://github.com/maszhongming/ParaKnowTransfer"
] | Large Language Models (LLMs) inherently encode a wealth of knowledge within their parameters through pre-training on extensive corpora. While prior research has delved into operations on these parameters to manipulate the underlying implicit knowledge—encompassing detection, editing, and merging—there remains an ambiguous understanding regarding their transferability across models with varying scales. In this paper, we seek to empirically investigate knowledge transfer from larger to smaller models through a parametric perspective. To achieve this, we employ sensitivity-based techniques to extract and align knowledge-specific parameters between different LLMs. Moreover, the LoRA module is used as the intermediary mechanism for injecting the extracted knowledge into smaller models. Evaluations across four benchmarks validate the efficacy of our proposed method. Our findings highlight the critical factors contributing to the process of parametric knowledge transfer, underscoring the transferability of model parameters across LLMs of different scales. | [] | [] | Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective | [
"Ming Zhong",
"Chenxin An",
"Weizhu Chen",
"Jiawei Han",
"Pengcheng He"
] | 2310.11451 | 17,899 | https://openreview.net/forum?id=mIEHIcHGOo |
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[] | Poster | [] | We present Emu, a multimodal foundation model that seamlessly generates images and text in multimodal context. This omnivore model can take in any single- modality or multimodal data input indiscriminately (e.g., interleaved image, text and video) through a one-model-for-all autoregressive training process. First, visual signals are encoded into embeddings, and together with text tokens form an interleaved input sequence. Emu is end-to-end trained with a unified objective of classifying the next text token or regressing the next visual embedding in the multimodal sequence. This versatile multimodality empowers the leverage of diverse pretraining data sources at scale, such as videos with interleaved frames and text, webpages with interleaved images and text, as well as web-scale image-text pairs and video-text pairs. Emu can serve as a generalist multimodal interface for both image-to-text and text-to-image tasks, supporting in-context image and text generation. Across a broad range of zero-shot/few-shot tasks including image captioning, visual question answering, video question answering and text-to-image generation, Emu demonstrates superb performance compared to state-of-the-art large multimodal models. Extended capabilities such as multimodal assistants via instruction tuning are also demonstrated with impressive performance. | [] | [] | Emu: Generative Pretraining in Multimodality | [
"Quan Sun",
"Qiying Yu",
"Yufeng Cui",
"Fan Zhang",
"Xiaosong Zhang",
"Yueze Wang",
"Hongcheng Gao",
"Jingjing Liu",
"Tiejun Huang",
"Xinlong Wang"
] | 17,898 | https://openreview.net/forum?id=mL8Q9OOamV |
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[] | Poster | [] | Equilibrium propagation (EP) is a compelling alternative to the back propagation of error algorithm (BP) for computing gradients of neural networks on biological or analog neuromorphic substrates. Still, the algorithm requires weight symmetry and infinitesimal equilibrium perturbations, i.e., nudges, to yield unbiased gradient estimates.Both requirements are challenging to implement in physical systems.Yet, whether and how weight asymmetry contributes to bias is unknown because, in practice, its contribution may be masked by a finite nudge. To address this question, we study generalized EP, which can be formulated without weight symmetry, and analytically isolate the two sources of bias.For complex-differentiable non-symmetric networks, we show that bias due to finite nudge can be avoided by estimating exact derivatives via a Cauchy integral.In contrast, weight asymmetry induces residual bias through poor alignment of EP's neuronal error vectors compared to BP resulting in low task performance.To mitigate the latter issue, we present a new homeostatic objective that directly penalizes functional asymmetries of the Jacobian at the network's fixed point. This homeostatic objective dramatically improves the network's ability to solve complex tasks such as ImageNet 32$\times$32. Our results lay the theoretical groundwork for studying and mitigating the adverse effects of imperfections of physical networks on learning algorithms that rely on the substrate's relaxation dynamics. | [] | [] | Improving equilibrium propagation without weight symmetry through Jacobian homeostasis | [
"Axel Laborieux",
"Friedemann Zenke"
] | 2309.02214 | 17,971 | https://openreview.net/forum?id=kUveo5k1GF |
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[] | Poster | [] | Unsupervised learning of object-centric representations in dynamic visual scenes is challenging. Unlike most previous approaches that learn to decompose 2D images, we present DynaVol, a 3D scene generative model that unifies geometric structures and object-centric learning in a differentiable volume rendering framework. The key idea is to perform object-centric voxelization to capture the 3D nature of the scene, which infers the probability distribution over objects at individual spatial locations. These voxel features evolve over time through a canonical-space deformation function, forming the basis for global representation learning via slot attention. The voxel features and global features are complementary and are both leveraged by a compositional NeRF decoder for volume rendering. DynaVol remarkably outperforms existing approaches for unsupervised dynamic scene decomposition. Once trained, the explicitly meaningful voxel features enable additional capabilities that 2D scene decomposition methods cannot achieve: it is possible to freely edit the geometric shapes or manipulate the motion trajectories of the objects. | [] | [] | DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric Voxelization | [
"Yanpeng Zhao",
"Siyu Gao",
"Yunbo Wang",
"Xiaokang Yang"
] | 2305.00393 | 17,963 | https://openreview.net/forum?id=koYsgfEwCQ |
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[] | Spotlight Poster | [] | *Humans are social beings*; we pursue social goals in our daily interactions, which is a crucial aspect of social intelligence. Yet, AI systems' abilities in this realm remain elusive. We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence. In our environment, agents role-play and *interact* under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals. We simulate the role-play interaction between LLM-based agents and humans within this task space and evaluate their performance with a holistic evaluation framework called SOTOPIA-Eval. With SOTOPIA, we find significant differences between these models in terms of their social intelligence, and we identify a subset of SOTOPIA scenarios, SOTOPIA-hard, that is generally challenging for all models. We find that on this subset, GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills. These findings demonstrate SOTOPIA's promise as a general platform for research on evaluating and improving social intelligence in artificial agents. | [] | [] | SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents | [
"Xuhui Zhou",
"Hao Zhu",
"Leena Mathur",
"Ruohong Zhang",
"Haofei Yu",
"Zhengyang Qi",
"Louis-Philippe Morency",
"Yonatan Bisk",
"Daniel Fried",
"Graham Neubig",
"Maarten Sap"
] | 2310.11667 | 17,897 | https://openreview.net/forum?id=mM7VurbA4r |
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[] | Poster | [] | Following the success of Large Language Models (LLMs), Large Multimodal Models (LMMs), such as the Flamingo model and its subsequent competitors, have started to emerge as natural step towards generalist agents. However, interacting with recent LMMs reveals major limitations that are hardly captured by the current evaluation benchmarks. Indeed, task performances (e.g., VQA accuracy) alone do not provide enough clues to understand their real capabilities, limitations, and to which extent such models are aligned to human expectations. To refine our understanding on those flaws, we deviate from the current evaluation paradigm, and (1) evaluate 8 recent open-source LMMs (based on the Flamingo architecture such as OpenFlamingo and IDEFICS) on 5 different axes; hallucinations, abstention, compositionality, explainability and instruction following. Our evaluation on these axes reveals major flaws in LMMs. To efficiently address these problems, and inspired by the success of In-Context Learning (ICL) in LLMs, (2) we explore ICL as a solution, and study how it affects these limitations. Based on our ICL study, (3) we push ICL further, and propose new multimodal ICL approaches such as; Multitask-ICL, Chain-of-Hindsight-ICL and Self-Correcting-ICL. Our findings are as follows. (1) Despite their success, LMMs have flaws that remain unsolved with scaling alone. (2) The effect of ICL on LMMs flaws is nuanced; despite its effectiveness for improved explainability, abstention and instruction following, ICL does not improve compositional abilities, and actually even amplifies hallucinations. (3) The proposed ICL variants are promising as post-hoc approaches to efficiently tackle some of those flaws. The code will be made public. | [] | [] | Beyond task performance: evaluating and reducing the flaws of large multimodal models with in-context-learning | [
"Mustafa Shukor",
"Alexandre Rame",
"Corentin Dancette",
"Matthieu Cord"
] | 2310.00647 | 17,896 | https://openreview.net/forum?id=mMaQvkMzDi |
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[] | Poster | [] | Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts describing complex scenes with multiple objects. While excelling in generating images from short, single-object descriptions, these models often struggle to faithfully capture all the nuanced details within longer and more elaborate textual inputs. In response, we present a novel approach leveraging Large Language Models (LLMs) to extract critical components from text prompts, including bounding box coordinates for foreground objects, detailed textual descriptions for individual objects, and a succinct background context. These components form the foundation of our layout-to-image generation model, which operates in two phases. The initial Global Scene Generation utilizes object layouts and background context to create an initial scene but often falls short in faithfully representing object characteristics as specified in the prompts. To address this limitation, we introduce an Iterative Refinement Scheme that iteratively evaluates and refines box-level content to align them with their textual descriptions, recomposing objects as needed to ensure consistency. Our evaluation on complex prompts featuring multiple objects demonstrates a substantial improvement in recall compared to baseline diffusion models. This is further validated by a user study, underscoring the efficacy of our approach in generating coherent and detailed scenes from intricate textual inputs. Our iterative framework offers a promising solution for enhancing text-to-image generation models' fidelity with lengthy, multifaceted descriptions, opening new possibilities for accurate and diverse image synthesis from textual inputs. | [] | [] | LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts | [
"Hanan Gani",
"Shariq Farooq Bhat",
"Muzammal Naseer",
"Salman Khan",
"Peter Wonka"
] | 2310.10640 | 17,895 | https://openreview.net/forum?id=mNYF0IHbRy |
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[] | Poster | [] | Understanding the functional organization of higher visual cortex is a central focus in neuroscience. Past studies have primarily mapped the visual and semantic selectivity of neural populations using hand-selected stimuli, which may potentially bias results towards pre-existing hypotheses of visual cortex functionality. Moving beyond conventional approaches, we introduce a data-driven method that generates natural language descriptions for images predicted to maximally activate individual voxels of interest. Our method -- Semantic Captioning Using Brain Alignments ("BrainSCUBA") -- builds upon the rich embedding space learned by a contrastive vision-language model and utilizes a pre-trained large language model to generate interpretable captions. We validate our method through fine-grained voxel-level captioning across higher-order visual regions. We further perform text-conditioned image synthesis with the captions, and show that our images are semantically coherent and yield high predicted activations. Finally, to demonstrate how our method enables scientific discovery, we perform exploratory investigations on the distribution of "person" representations in the brain, and discover fine-grained semantic selectivity in body-selective areas. Unlike earlier studies that decode text, our method derives *voxel-wise captions of semantic selectivity*. Our results show that BrainSCUBA is a promising means for understanding functional preferences in the brain, and provides motivation for further hypothesis-driven investigation of visual cortex. | [] | [] | BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity | [
"Andrew Luo",
"Margaret Marie Henderson",
"Michael J. Tarr",
"Leila Wehbe"
] | 2310.04420 | 17,893 | https://openreview.net/forum?id=mQYHXUUTkU |
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[] | Poster | [] | Adversarial robustness is an important standard for measuring the quality of learned models, and adversarial training is an effective strategy for improving the adversarial robustness of models. In this paper, we disclose that adversarially trained models are vulnerable to two-faced attacks, where slight perturbations in input features are crafted to make the model exhibit a false sense of robustness in the verification phase. Such a threat is significantly important as it can mislead our evaluation of the adversarial robustness of models, which could cause unpredictable security issues when deploying substandard models in reality. More seriously, this threat seems to be pervasive and tricky: we find that many types of models suffer from this threat, and models with higher adversarial robustness tend to be more vulnerable. Furthermore, we provide the first attempt to formulate this threat, disclose its relationships with adversarial risk, and try to circumvent it via a simple countermeasure. These findings serve as a crucial reminder for practitioners to exercise caution in the verification phase, urging them to refrain from blindly trusting the exhibited adversarial robustness of models. | [] | [] | On the Vulnerability of Adversarially Trained Models Against Two-faced Attacks | [
"Shengjie Zhou",
"Lue Tao",
"Yuzhou Cao",
"Tao Xiang",
"Bo An",
"Lei Feng"
] | 17,892 | https://openreview.net/forum?id=mXpNp8MMr5 |
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[] | Poster | [] | Learning-to-match (LTM) is an effective inverse optimal transport framework for learning the underlying ground metric between two sources of data, which can be further used to form the matching between them. Nevertheless, the conventional LTM framework is not scalable since it needs to use the entire dataset each time updating the parametric ground metric. To adapt the LTM framework to the deep learning setting, we propose the mini-batch learning-to-match (m-LTM) framework for audio-text retrieval problems, which is based on mini-batch subsampling and neural networks parameterized ground metric. In addition, we improve further the framework by introducing the Mahalanobis-enhanced family of ground metrics. Moreover, to cope with the noisy data correspondence problem arising from practice, we additionally propose a variant using partial optimal transport to mitigate the pairing uncertainty in training data. We conduct extensive experiments on audio-text matching problems usingthree datasets: AudioCaps, Clotho, and ESC-50. Results demonstrate that our proposed method is capable of learning rich and expressive joint embedding space, which achieves SOTA performance. Beyond this, the proposed m-LTM framework is able to close the modality gap across audio and text embedding, which surpasses both triplet and contrastive loss in the zero-shot sound event detection task on the ESC-50 dataset. Finally, our strategy to use partial OT with m-LTM has shown to be more noise tolerance than contrastive loss under a variant of noise ratio of training data in AudioCaps. | [] | [] | Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation | [
"Manh Luong",
"Khai Nguyen",
"Nhat Ho",
"Reza Haf",
"Dinh Phung",
"Lizhen Qu"
] | 17,948 | https://openreview.net/forum?id=l60EM8md3t |
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[] | Spotlight Poster | [] | Recent progress in vision language foundation models has shown their ability to understand multimodal data and resolve complicated vision language tasks, including robotics manipulation. We seek a straightforward way of making use of existing vision-language models (VLMs) with simple fine-tuning on robotics data.To this end, we derive a simple and novel vision-language manipulation framework, dubbed RoboFlamingo, built upon the open-source VLMs, OpenFlamingo. Unlike prior works, RoboFlamingo utilizes pre-trained VLMs for single-step vision-language comprehension, models sequential history information with an explicit policy head, and is slightly fine-tuned by imitation learning only on language-conditioned manipulation datasets. Such a decomposition provides RoboFlamingo the flexibility for open-loop control and deployment on low-performance platforms. By exceeding the state-of-the-art performance with a large margin on the tested benchmark, we show RoboFlamingo can be an effective and competitive alternative to adapt VLMs to robot control.Our extensive experimental results also reveal several interesting conclusions regarding the behavior of different pre-trained VLMs on manipulation tasks. We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy. Our code will be made public upon acceptance. | [] | [] | Vision-Language Foundation Models as Effective Robot Imitators | [
"Xinghang Li",
"Minghuan Liu",
"Hanbo Zhang",
"Cunjun Yu",
"Jie Xu",
"Hongtao Wu",
"Chilam Cheang",
"Ya Jing",
"Weinan Zhang",
"Huaping Liu",
"Hang Li",
"Tao Kong"
] | 2311.01378 | 17,943 | https://openreview.net/forum?id=lFYj0oibGR |
|
[] | Spotlight Poster | [] | High accuracy, low latency and high energy efficiency represent a set of contradictory goals when searching for system solutions for image classification and detection. While high-quality images naturally result in more precise detection and classification, they also result in a heavier computational workload for imaging and processing, reduce camera refresh rates, and increase the volume of data communication between the camera and processor. Taking inspiration from the foveal-peripheral sampling mechanism, saccade mechanism observed in the human visual system and the filling-in phenomena of brain, we have developed an active scene reconstruction architecture based on multiple foveal views. This model stitches together information from foveal and peripheral vision, which are sampled from multiple glances. Assisted by a reinforcement learning-based saccade mechanism, our model reduces the required input pixels by over 90\% per frame while maintaining the same level of performance in image recognition as with the original images. We evaluated the effectiveness of our model using the GTSRB dataset and the ImageNet dataset. Using an equal number of input pixels, our study demonstrates a 5\% higher image recognition accuracy compared to state-of-the-art foveal-peripheral vision systems. Furthermore, we demonstrate that our foveal sampling/saccadic scene reconstruction model exhibits significantly lower complexity and higher data efficiency during the training phase compared to existing approaches. | [] | [] | Improved Efficiency Based on Learned Saccade and Continuous Scene Reconstruction From Foveated Visual Sampling | [
"Jiayang Liu",
"Yiming Bu",
"Daniel Tso",
"Qinru Qiu"
] | 17,931 | https://openreview.net/forum?id=lOwkOIUJtx |
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[] | Poster | [] | Convolutional neural network (CNN) is easily affected by backdoor injections, whose models perform normally on clean samples but produce specific outputs on poisoned ones. Most of the existing studies have focused on the effect of trigger feature changes of poisoned samples on model generalization in spatial domain. We focus on the mechanism of CNN memorize poisoned samples in frequency domain, and find that CNN generate generalization to poisoned samples by memorizing the frequency domain distribution of trigger changes. We also explore the influence of trigger perturbations in different frequency domain components on the generalization of poisoned models from visible and invisible backdoor attacks, and prove that high-frequency components are more susceptible to perturbations than low-frequency components. Based on the above fundings, we propose a universal invisible strategy for visible triggers, which can achieve trigger invisibility while maintaining raw attack performance. We also design a novel frequency domain backdoor attack method based on low-frequency semantic information, which can achieve 100\% attack accuracy on multiple models and multiple datasets, and can bypass multiple defenses. | [] | [] | Rethinking CNN’s Generalization to Backdoor Attack from Frequency Domain | [
"Quanrui Rao",
"Lin Wang",
"Wuying Liu"
] | 17,890 | https://openreview.net/forum?id=mYhH0CDFFa |
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[] | Poster | [] | Conventional wisdom suggests that neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs. Our work reassesses this assumption for neural networks with high-dimensional inputs. Rather than extrapolating in arbitrary ways, we observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD. Moreover, we find that this value often closely approximates the optimal constant solution (OCS), i.e., the prediction that minimizes the average loss over the training data without observing the input. We present results showing this phenomenon across 8 datasets with different distributional shifts (including CIFAR10-C and ImageNet-R, S), different loss functions (cross entropy, MSE, and Gaussian NLL), and different architectures (CNNs and transformers). Furthermore, we present an explanation for this behavior, which we first validate empirically and then study theoretically in a simplified setting involving deep homogeneous networks with ReLU activations. Finally, we show how one can leverage our insights in practice to enable risk-sensitive decision-making in the presence of OOD inputs. | [] | [] | Deep Neural Networks Tend To Extrapolate Predictably | [
"Katie Kang",
"Amrith Setlur",
"Claire Tomlin",
"Sergey Levine"
] | 2310.00873 | 17,921 | https://openreview.net/forum?id=ljwoQ3cvQh |
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[] | Poster | [] | Text-guided diffusion models have become a popular tool in image synthesis, known for producing high-quality and diverse images. However, their application to editing real images often encounters hurdles primarily due to the text condition deteriorating the reconstruction quality and subsequently affecting editing fidelity. Null-text Inversion (NTI) has made strides in this area, but it fails to capture spatial context and requires computationally intensive per-timestep optimization. Addressing these challenges, we present Noise Map Guidance (NMG), an inversion method rich in a spatial context, tailored for real-image editing. Significantly, NMG achieves this without necessitating optimization, yet preserves the editing quality. Our empirical investigations highlight NMG's adaptability across various editing techniques and its robustness to variants of DDIM inversions. | [] | [] | Noise Map Guidance: Inversion with Spatial Context for Real Image Editing | [
"Hansam Cho",
"Jonghyun Lee",
"Seoung Bum Kim",
"Tae-Hyun Oh",
"Yonghyun Jeong"
] | 2402.04625 | 17,887 | https://openreview.net/forum?id=mhgm0IXtHw |
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[] | Spotlight Poster | [] | Developing equivariant neural networks for the E(3) group plays a pivotal role in modeling 3D data across real-world applications. Enforcing this equivariance primarily involves the tensor products of irreducible representations (irreps). However, the computational complexity of such operations increases significantly as higher-order tensors are used. In this work, we propose a systematic approach to substantially accelerate the computation of the tensor products of irreps. We mathematically connect the commonly used Clebsch-Gordan coefficients to the Gaunt coefficients, which are integrals of products of three spherical harmonics. Through Gaunt coefficients, the tensor product of irreps becomes equivalent to the multiplication between spherical functions represented by spherical harmonics. This perspective further allows us to change the basis for the equivariant operations from spherical harmonics to a 2D Fourier basis. Consequently, the multiplication between spherical functions represented by a 2D Fourier basis can be efficiently computed via the convolution theorem and Fast Fourier Transforms. This transformation reduces the complexity of full tensor products of irreps from $\mathcal{O}(L^6)$ to $\mathcal{O}(L^3)$, where $L$ is the max degree of irreps. Leveraging this approach, we introduce the Gaunt Tensor Product, which serves as a new method to construct efficient equivariant operations across different model architectures. Our experiments demonstrate both the superior efficiency and improved performance of our approach on a range of tasks. The code and models will be made publicly available. | [] | [] | Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products | [
"Shengjie Luo",
"Tianlang Chen",
"Aditi S. Krishnapriyan"
] | 2401.10216 | 17,886 | https://openreview.net/forum?id=mhyQXJ6JsK |
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[] | Poster | [] | Retrieval-augmented language models improve language models (LMs) by retrieving documents and prepending them in-context.However, these documents, often spanning hundreds of words, make inference substantially less efficient. We propose compressing the retrieved documents into textual summaries prior to in-context integration. This not only reduces the computational costs but also relieve the burden of LMs to identify relevant information in long retrieved documents. We present two compressors -- an extractive compressor which selects useful sentences from retrieved documents and an abstractive compressor which generates summary by synthesizing information from multiple documents. Both are trained to achieve performance gain in LMs when we prepend the generated summary from the compressor to LMs' input, while minimizing the summary length. When retrieved documents are irrelevant to the input or offer no additional information to LM, our compressors output an empty string, enabling selective augmentation. We evaluate our approach on the language modeling task and open domain question answering task. We achieve a compression rate of as low as 6% with minimal loss in performance for both tasks, significantly outperforming the off-the-shelf summarization models. We show that our compressors trained for one LM can transfer to other LMs on the language modeling task and provide a summary largely faithful to the retrieved documents. | [] | [] | RECOMP: Improving Retrieval-Augmented LMs with Context Compression and Selective Augmentation | [
"Fangyuan Xu",
"Weijia Shi",
"Eunsol Choi"
] | 17,885 | https://openreview.net/forum?id=mlJLVigNHp |
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[] | Poster | [] | The diffusion model has been successfully used in many computer vision applications, such as text-guided image generation and image-to-image translation. Recently, there have been attempts on extending the diffusion model for time series data. However, these extensions are fairly straightforward and do not utilize the unique properties of time series data. As different patterns are usually exhibited at multiple scales of a time series, we in this paper leverage this multi-resolution temporal structure and propose the \underline{m}ulti-\underline{r}esolution \underline{diff}usion model (\texttt{mr-Diff}). By using the seasonal-trend decomposition, we sequentially extract fine-to-coarse trends from the time series for forward diffusion. The denoising process then proceeds in an easy-to-hard non-autoregressive manner. The coarsest trend is generated first. Finer details are progressively added, using the predicted coarser trends as condition variables. Experimental results on nine real-world time series datasets demonstrate that \texttt{mr-Diff} outperforms state-of-the-art time series diffusion models. It is also better than or comparable across a wide variety of advanced time series prediction models. | [] | [] | Multi-Resolution Diffusion Models for Time Series Forecasting | [
"Lifeng Shen",
"Weiyu Chen",
"James Kwok"
] | 17,883 | https://openreview.net/forum?id=mmjnr0G8ZY |
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[] | Poster | [] | Recent studies have shown competitive performance in protein inverse folding, while most of them disregard the importance of predictive confidence, fail to cover the vast protein space, and do not incorporate common protein knowledge. Given the great success of pretrained models on diverse protein-related tasks and the fact that recovery is highly correlated with confidence, we wonder whether this knowledge can push the limits of protein design further. As a solution, we propose a knowledge-aware module that refines low-quality residues. We also introduce a memory-retrieval mechanism to save more than 50\% of the training time. We extensively evaluate our proposed method on the CATH, TS50, TS500, and PDB datasets and our results show that our KW-Design method outperforms the previous PiFold method by approximately 9\% on the CATH dataset. KW-Design is the first method that achieves 60+\% recovery on all these benchmarks. We also provide additional analysis to demonstrate the effectiveness of our proposed method. The code will be publicly available upon acceptance. | [] | [] | KW-Design: Pushing the Limit of Protein Design via Knowledge Refinement | [
"Zhangyang Gao",
"Cheng Tan",
"Xingran Chen",
"Yijie Zhang",
"Jun Xia",
"Siyuan Li",
"Stan Z. Li"
] | 17,881 | https://openreview.net/forum?id=mpqMVWgqjn |
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[] | Poster | [] | This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-ups across 12 LLMs, but it can also potentially improve the answer quality on several question categories. SoT is an initial attempt at data-centric optimization for inference efficiency, and further underscores the potential of pushing LLMs to think more like a human for answer quality. | [] | [] | Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation | [
"Xuefei Ning",
"Zinan Lin",
"Zixuan Zhou",
"Zifu Wang",
"Huazhong Yang",
"Yu Wang"
] | 17,880 | https://openreview.net/forum?id=mqVgBbNCm9 |
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[] | Poster | [] | Modern distributed training relies heavily on communication compression to reduce the communication overhead. In this work, we study algorithms employing a popular class of contractive compressors in order to reduce communication overhead. However, the naive implementation often leads to unstable convergence or even exponential divergence due to the compression bias. Error Compensation (EC) is an extremely popular mechanism to mitigate the aforementioned issues during the training of models enhanced by contractive compression operators. Compared to the effectiveness of EC in the data homogeneous regime, the understanding of the practicality and theoretical foundations of EC in the data heterogeneous regime is limited. Existing convergence analyses typically rely on strong assumptions such as bounded gradients, bounded data heterogeneity, or large batch accesses, which are often infeasible in modern Machine Learning Applications. We resolve the majority of current issues by proposing EControl, a novel mechanism that can regulate error compensation by controlling the strength of the feedback signal. We prove fast convergence for EControl in standard strongly convex, general convex, and nonconvex settings without any additional assumptions on the problem or data heterogeneity. We conduct extensive numerical evaluations to illustrate the efficacy of our method and support our theoretical findings. | [] | [] | EControl: Fast Distributed Optimization with Compression and Error Control | [
"Yuan Gao",
"Rustem Islamov",
"Sebastian U Stich"
] | 2311.05645 | 17,916 | https://openreview.net/forum?id=lsvlvWB9vz |
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[] | Spotlight Poster | [] | Training deep networks requires various design decisions regarding for instance their architecture, data augmentation, or optimization. In this work, we find these training variations to result in networks learning unique feature sets from the data. Using public model libraries comprising thousands of models trained on canonical datasets like ImageNet, we observe that for arbitrary pairings of pretrained models, one model extracts significant data context unavailable in the other – independent of overall performance. Given any arbitrary pairing of pretrained models and no external rankings (such as separate test sets, e.g. due to data privacy), we investigate if it is possible to transfer such "complementary" knowledge from one model to another without performance degradation – a task made particularly difficult as additional knowledge can be contained in stronger, equiperformant or weaker models. Yet facilitating robust transfer in scenarios agnostic to pretrained model pairings would unlock auxiliary gains and knowledge fusion from any model repository without restrictions on model and problem specifics - including from weaker, lower-performance models. This work therefore provides an initial, in-depth exploration on the viability of such general-purpose knowledge transfer. Across large-scale experiments, we first reveal the shortcomings of standard knowledge distillation techniques, and then propose a much more general extension through data partitioning for successful transfer between nearly all pretrained models, which we show can also be done unsupervised. Finally, we assess both the scalability and impact of fundamental model properties on successful model-agnostic knowledge transfer. | [] | [] | Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model | [
"Karsten Roth",
"Lukas Thede",
"A. Sophia Koepke",
"Oriol Vinyals",
"Olivier J Henaff",
"Zeynep Akata"
] | 2310.17653 | 17,907 | https://openreview.net/forum?id=m50eKHCttz |
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[] | Poster | [
"https://github.com/lokali/FedCDH.git"
] | Conventional causal discovery methods rely on centralized data, which is inconsistent with the decentralized nature of data in many real-world situations. This discrepancy has motivated the development of federated causal discovery (FCD) approaches. However, existing FCD methods may be limited by their potentially restrictive assumptions of identifiable functional causal models or homogeneous data distributions, narrowing their applicability in diverse scenarios. In this paper, we propose a novel FCD method attempting to accommodate arbitrary causal models and heterogeneous data. We first utilize a surrogate variable corresponding to the client index to account for the data heterogeneity across different clients. We then develop a federated conditional independence test (FCIT) for causal skeleton discovery and establish a federated independent change principle (FICP) to determine causal directions. These approaches involve constructing summary statistics as a proxy of the raw data to protect data privacy. Owing to the nonparametric properties, FCIT and FICP make no assumption about particular functional forms, thereby facilitating the handling of arbitrary causal models. We conduct extensive experiments on synthetic and real datasets to show the efficacy of our method. The code is available at \url{https://github.com/lokali/FedCDH.git}. | [] | [] | Federated Causal Discovery from Heterogeneous Data | [
"Longkang Li",
"Ignavier Ng",
"Gongxu Luo",
"Biwei Huang",
"Guangyi Chen",
"Tongliang Liu",
"Bin Gu",
"Kun Zhang"
] | 2402.13241 | 17,904 | https://openreview.net/forum?id=m7tJxajC3G |
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[] | Poster | [] | Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are limited to small degrees of equivariant representations due to their computational complexity. In this paper, we investigate whether these architectures can scale well to higher degrees. Starting from Equiformer, we first replace $SO(3)$ convolutionswith eSCN convolutions to efficiently incorporate higher-degree tensors. Then, to better leverage the power of higher degrees, we propose three architectural improvements – attention re-normalization, separable $S^2$ activation and separable layer normalization. Putting these all together, we propose EquiformerV2, which outperforms previous state-of-the-art methods on large-scale OC20 dataset by up to 9% on forces, 4% on energies, offers better speed-accuracy trade-offs, and 2$\times$ reduction in DFT calculations needed for computing adsorption energies. Additionally, EquiformerV2 trained on only OC22 dataset outperforms GemNet-OC trained on both OC20 and OC22 datasets, achieving much better data efficiency. Finally, we compare EquiformerV2 with Equiformer on QM9 and OC20 S2EF-2Mdatasets to better understand the performance gain brought by higher degrees. | [] | [] | EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations | [
"Yi-Lun Liao",
"Brandon M Wood",
"Abhishek Das",
"Tess Smidt"
] | 2306.12059 | 17,903 | https://openreview.net/forum?id=mCOBKZmrzD |
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[] | Poster | [] | Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks have been developed to understand what constitutes an effective representation. However, the relationships between these methods and the shared properties among them remain unclear. In this paper, we show that many of these seemingly distinct methods and frameworks for state and history abstractions are, in fact, based on a common idea of self-predictive abstraction. Furthermore, we provide theoretical insights into the widely adopted objectives and optimization, such as the stop-gradient technique, in learning self-predictive representations. These findings together yield a minimalist algorithm to learn self-predictive representations for states and histories. We validate our theories by applying our algorithm to standard MDPs, distracted MDPs, and sparse-reward POMDPs. | [] | [] | Bridging State and History Representations: Understanding Self-Predictive RL | [
"Tianwei Ni",
"Benjamin Eysenbach",
"Erfan SeyedSalehi",
"Michel Ma",
"Clement Gehring",
"Aditya Mahajan",
"Pierre-Luc Bacon"
] | 2401.08898 | 17,879 | https://openreview.net/forum?id=ms0VgzSGF2 |
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[] | Poster | [] | Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively concentrated on increasing the quantity and functional correctness of training sets while disregarding other stylistic elements of programs. More recently, data quality has garnered a lot of interest and multiple works have showcased its importance for improving performance. In this work, we investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system. We build a novel data-cleaning pipeline that uses these principles to transform existing programs by 1.) renaming variables, 2.) modularizing and decomposing complex code into smaller helper sub-functions, and 3.) inserting natural-language based planning annotations. We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B on our transformed programs improves the performance by up to \textbf{30\%} compared to fine-tuning on the original dataset. Additionally, we demonstrate improved performance from using a smaller amount of higher-quality data, finding that a model fine-tuned on the entire original dataset is outperformed by a model trained on one-eighth of our cleaned dataset. Even in comparison to closed-source models, our models outperform the much larger AlphaCode models. | [] | [] | LLM-Assisted Code Cleaning For Training Accurate Code Generators | [
"Naman Jain",
"Tianjun Zhang",
"Wei-Lin Chiang",
"Joseph E. Gonzalez",
"Koushik Sen",
"Ion Stoica"
] | 2311.14904 | 17,888 | https://openreview.net/forum?id=maRYffiUpI |
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[] | Poster | [] | We consider the domain adaptation problem in the context of label shift, where the label distributions between source and target domain differ, but the conditional distributions of features given the label are the same. To solve the label shift adaption problem, we develop a novel matching framework named \textit{class probability matching} (\textit{CPM}). It is inspired by a new understanding of the source domain's class probability, as well as a specific relationship between class probability ratios and feature probability ratios between the source and target domains. CPM is able to maintain the same theoretical guarantee with the existing feature probability matching framework, while significantly improving the computational efficiency due to directly matching the probabilities of the label variable. Within the CPM framework, we propose an algorithm named \textit{class probability matching with calibrated networks} (\textit{CPMCN}) for target domain classification. From the theoretical perspective, we establish the generalization bound of the CPMCN method in order to explain the benefits of introducing calibrated networks. From the experimental perspective, real data comparisons show that CPMCN outperforms existing matching-based and EM-based algorithms. | [] | [] | Class Probability Matching with Calibrated Networks for Label Shift Adaption | [
"Hongwei Wen",
"Annika Betken",
"Hanyuan Hang"
] | 17,884 | https://openreview.net/forum?id=mliQ2huFrZ |
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[] | Poster | [] | In applying Reinforcement Learning (RL) to robot trajectory generation, two key challenges commonly emerge. First, the stochastic exploration strategies of step-based RL are unable to produce high-order smooth trajectories. Second, existing methods struggle with effectively modeling movement correlations among different time steps and degrees of freedom, which are crucial for complex tasks and safety measures. Episodic RL methods address these challenges by employing parameterized trajectory generators like Movement Primitives (MP), framing the problem as contextual optimization. While effective in generating smooth trajectories and capturing some movement correlations, these methods lack in utilizing temporal structure within trajectories, resulting in suboptimal sample efficiency. We introduce the Temporally-Correlated Episodic RL (TCE) method to address these shortcomings. TCE enhances exploration efficiency by sampling multi-second trajectories in a parameterized space, thereby assuring high-order trajectory smoothness and the capture of movement correlations. For policy updates, TCE breaks down the entire trajectory into smaller segments, evaluating each segment on its distinct advantages. This nuanced approach allows us to utilize temporal information obtained during trajectory execution.We validate the effectiveness of TCE through experiments on various robotic manipulation tasks, showing its advantages over step-based and episodic RL approaches. | [] | [] | Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning | [
"Ge Li",
"Hongyi Zhou",
"Dominik Roth",
"Serge Thilges",
"Fabian Otto",
"Rudolf Lioutikov",
"Gerhard Neumann"
] | 2401.11437 | 17,882 | https://openreview.net/forum?id=mnipav175N |
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[] | Poster | [] | Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., healthcare, financial, as well as scientific projects that lack a centralized data facility. Nonetheless, because of the disparity of computing resources among different clients (i.e., device heterogeneity), synchronous federated learning algorithms suffer from degraded efficiency when waiting for straggler clients. Similarly, asynchronous federated learning algorithms experience degradation in the convergence rate and final model accuracy on non-identically and independently distributed (non-IID) heterogeneous datasets due to stale local models and client drift. To address these limitations in cross-silo federated learning with heterogeneous clients and data, we propose FedCompass, an innovative semi-asynchronous federated learning algorithm with a computing power-aware scheduler on the server side, which adaptively assigns varying amounts of training tasks to different clients using the knowledge of the computing power of individual clients. FedCompass ensures that multiple locally trained models from clients are received almost simultaneously as a group for aggregation, effectively reducing the staleness of local models. At the same time, the overall training process remains asynchronous, eliminating prolonged waiting periods from straggler clients. Using diverse non-IID heterogeneous distributed datasets, we demonstrate that FedCompass achieves faster convergence and higher accuracy than other asynchronous algorithms while remaining more efficient than synchronous algorithms when performing federated learning on heterogeneous clients. | [] | [] | FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler | [
"Zilinghan Li",
"Pranshu Chaturvedi",
"Shilan He",
"Han Chen",
"Gagandeep Singh",
"Volodymyr Kindratenko",
"Eliu A Huerta",
"Kibaek Kim",
"Ravi Madduri"
] | 2309.14675 | 17,878 | https://openreview.net/forum?id=msXxrttLOi |
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[] | Poster | [] | Zero-shot text-to-speech (TTS) aims to synthesize voices with unseen speech prompts, which significantly reduces the data and computation requirements for voice cloning by skipping the fine-tuning process. However, the prompting mechanisms of zero-shot TTS still face challenges in the following aspects: 1) previous works of zero-shot TTS are typically trained with single-sentence prompts, which significantly restricts their performance when the data is relatively sufficient during the inference stage. 2) The prosodic information in prompts is highly coupled with timbre, making it untransferable to each other. This paper introduces Mega-TTS, a generic prompting mechanism for zero-shot TTS, to tackle the aforementioned challenges. Specifically, we design a powerful acoustic autoencoder that separately encodes the prosody and timbre information into the compressed latent space while providing high-quality reconstructions. Then, we propose a multi-reference timbre encoder and a prosody latent language model (P-LLM) to extract useful information from multi-sentence prompts. We further leverage the probabilities derived from multiple P-LLM outputs to produce transferable and controllable prosody. Experimental results demonstrate that Mega-TTS could not only synthesize identity-preserving speech with a short prompt of an unseen speaker from arbitrary sources but consistently outperform the fine-tuning method when the volume of data ranges from 10 seconds to 5 minutes. Furthermore, our method enables to transfer various speaking styles to the target timbre in a fine-grained and controlled manner. Audio samples can be found in https://boostprompt.github.io/boostprompt/. | [] | [] | Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis | [
"Ziyue Jiang",
"Jinglin Liu",
"Yi Ren",
"Jinzheng He",
"Zhenhui Ye",
"Shengpeng Ji",
"Qian Yang",
"Chen Zhang",
"Pengfei Wei",
"Chunfeng Wang",
"Xiang Yin",
"Zejun MA",
"Zhou Zhao"
] | 2307.07218 | 17,876 | https://openreview.net/forum?id=mvMI3N4AvD |
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[] | Spotlight Poster | [] | Supervised learning methods have been found to exhibit inductive biases favoring simpler features. When such features are spuriously correlated with the label, this can result in suboptimal performance on minority subgroups. Despite the growing popularity of methods which learn from unlabeled data, the extent to which these representations rely on spurious features for prediction is unclear. In this work, we explore the impact of spurious features on Self-Supervised Learning (SSL) for visual representation learning. We first empirically show that commonly used augmentations in SSL can cause undesired invariances in the image space, and illustrate this with a simple example. We further show that classical approaches in combating spurious correlations, such as dataset re-sampling during SSL, do not consistently lead to invariant representations. Motivated by these findings, we propose LateTVG to remove spurious information from these representations during pre-training, by regularizing later layers of the encoder via pruning. We find that our method produces representations which outperform the baselines on several benchmarks, without the need for group or label information during SSL. | [] | [] | Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation | [
"Kimia Hamidieh",
"Haoran Zhang",
"Swami Sankaranarayanan",
"Marzyeh Ghassemi"
] | 17,877 | https://openreview.net/forum?id=mutJBk3ILg |
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[] | Spotlight Poster | [
"https://github.com/bigcode-project/octopack"
] | Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack. | [] | [] | OctoPack: Instruction Tuning Code Large Language Models | [
"Niklas Muennighoff",
"Qian Liu",
"Armel Randy Zebaze",
"Qinkai Zheng",
"Binyuan Hui",
"Terry Yue Zhuo",
"Swayam Singh",
"Xiangru Tang",
"Leandro Von Werra",
"Shayne Longpre"
] | 2308.07124 | 17,875 | https://openreview.net/forum?id=mw1PWNSWZP |
|
[] | Poster | [] | Continual learning has gained increasing importance as it facilitates the acquisition and refinement of scalable knowledge and skills in language models. However, existing methods typically encounter strict limitations and challenges in real-world scenarios, such as reliance on experience replay, optimization constraints, and inference task-ID. In this study, we introduce the Scalable Language Model (SLM) to overcome these limitations within a more challenging and generalized setting, representing a significant advancement toward practical applications for continual learning. Specifically, we propose the Joint Adaptive Re-Parameterization (JARe), integrated with Dynamic Task-related Knowledge Retrieval (DTKR), to enable adaptive adjustment of language models based on specific downstream tasks. This approach leverages the task distribution within the vector space, aiming to achieve a smooth and effortless continual learning process. Our method demonstrates state-of-the-art performance on diverse backbones and benchmarks, achieving effective continual learning in both full-set and few-shot scenarios with minimal forgetting. Moreover, while prior research primarily focused on a single task type such as classification, our study goes beyond, with the large language model, i.e., LLaMA-2, to explore the effects across diverse domains and task types, such that a single language model can be decently scaled to broader applications. The code and models will be released to the public. | [] | [] | Scalable Language Model with Generalized Continual Learning | [
"Bohao PENG",
"Zhuotao Tian",
"Shu Liu",
"Ming-Chang Yang",
"Jiaya Jia"
] | 2404.07470 | 17,874 | https://openreview.net/forum?id=mz8owj4DXu |
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[] | Poster | [] | In order to train networks for verified adversarial robustness, it is common to over-approximate the worst-case loss over perturbation regions, resulting in networks that attain verifiability at the expense of standard performance.As shown in recent work, better trade-offs between accuracy and robustness can be obtained by carefully coupling adversarial training with over-approximations. We hypothesize that the expressivity of a loss function, which we formalize as the ability to span a range of trade-offs between lower and upper bounds to the worst-case loss through a single parameter (the over-approximation coefficient), is key to attaining state-of-the-art performance. To support our hypothesis, we show that trivial expressive losses, obtained via convex combinations between adversarial attacks and IBP bounds, yield state-of-the-art results across a variety of settings in spite of their conceptual simplicity.We provide a detailed analysis of the relationship between the over-approximation coefficient and performance profiles across different expressive losses, showing that, while expressivity is essential, better approximations of the worst-case loss are not necessarily linked to superior robustness-accuracy trade-offs. | [] | [] | Expressive Losses for Verified Robustness via Convex Combinations | [
"Alessandro De Palma",
"Rudy R Bunel",
"Krishnamurthy Dj Dvijotham",
"M. Pawan Kumar",
"Robert Stanforth",
"Alessio Lomuscio"
] | 2305.13991 | 17,873 | https://openreview.net/forum?id=mzyZ4wzKlM |
|
[] | Poster | [] | Recent advances in high-fidelity virtual environments serve as one of the major driving forces for building intelligent embodied agents to perceive, reason and interact with the physical world. Typically, these environments remain unchanged unless agents interact with them. However, in real-world scenarios, agents might also face dynamically changing environments characterized by unexpected events and need to rapidly take action accordingly. To remedy this gap, we propose a new simulated embodied benchmark, called HAZARD, specifically designed to assess the decision-making abilities of embodied agents in dynamic situations. HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind, and specifically supports the utilization of large language models (LLMs) to assist common sense reasoning and decision-making. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines, including reinforcement learning (RL), rule-based, and search-based methods in dynamically changing environments. As a first step toward addressing this challenge using large language models, we further develop an LLM-based agent and perform an in-depth analysis of its promise and challenge of solving these challenging tasks. | [] | [] | HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments | [
"Qinhong Zhou",
"Sunli Chen",
"Yisong Wang",
"Haozhe Xu",
"Weihua Du",
"Hongxin Zhang",
"Yilun Du",
"Joshua B. Tenenbaum",
"Chuang Gan"
] | 2401.12975 | 17,872 | https://openreview.net/forum?id=n6mLhaBahJ |
|
[] | Spotlight Poster | [] | A graph is a structure made up of vertices and edges used to represent complex relationships between entities, while a graph stream is a continuous flow of graph updates that convey evolving relationships between entities. The massive volume and high dynamism of graph streams promote research on data structures of graph summarization, which provides a concise and approximate view of graph streams with sub-linear space and linear construction time, enabling real-time graph analytics in various domains, such as social networking, financing, and cybersecurity.In this work, we propose the Mayfly, the first neural data structure for summarizing graph streams. The Mayfly replaces handcrafted data structures with better accuracy and adaptivity.To cater to practical applications, Mayfly incorporates two offline training phases.During the larval phase, the Mayfly learns basic summarization abilities from automatically and synthetically constituted meta-tasks, and in the metamorphosis phase, it rapidly adapts to real graph streams via meta-tasks.With specific configurations of information pathways, the Mayfly enables flexible support for miscellaneous graph queries, including edge, node, and connectivity queries.Extensive empirical studies show that the Mayfly significantly outperforms its handcrafted competitors. | [] | [] | Mayfly: a Neural Data Structure for Graph Stream Summarization | [
"Yuan Feng",
"Yukun Cao",
"Wang Hairu",
"Xike Xie",
"S Kevin Zhou"
] | 17,871 | https://openreview.net/forum?id=n7Sr8SW4bn |
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[] | Spotlight Poster | [] | When applied to question answering and other text generation tasks, language models (LMs) may be queried generatively (by sampling answers from their output distribution) or discriminatively (by using them to score or rank a set of candidate answers). These procedures sometimes yield very different predictions. How do we reconcile mutually incompatible scoring procedures to obtain coherent LM predictions? We introduce a new, a training-free, game-theoretic procedure for language model decoding. Our approach casts language model decoding as a regularized imperfect-information sequential signaling game—which we term the concensus game—in which a generator seeks to communicate an abstract correctness parameter using natural language sentences to a discriminator. We develop computational procedures for finding approximate equilibria of this game, resulting in a decoding algorithm we call equilibrium-ranking. Applied to a large number of tasks (including reading comprehension, commonsense reasoning, mathematical problem-solving, and assistive dialog), equilibrium-ranking consistently improves performance over existing LM decoding procedures. These improvements are sometimes substantial—on multiple benchmarks, we observe that applying equilibrium-ranking to LLaMA-7B outperforms the much larger LLaMA-65B and PaLM-540B models. | [] | [] | The Consensus Game: Language Model Generation via Equilibrium Search | [
"Athul Paul Jacob",
"Yikang Shen",
"Gabriele Farina",
"Jacob Andreas"
] | 2310.09139 | 17,870 | https://openreview.net/forum?id=n9xeGcI4Yg |
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[] | Poster | [] | Federated Learning (FL) has gained significant attraction due to its ability to enable privacy-preserving training over decentralized data. Current literature in FL mostly focuses on single-task learning. However, over time, new tasks may appear in the clients and the global model should learn these tasks without forgetting previous tasks. This real-world scenario is known as Continual Federated Learning (CFL). The main challenge of CFL is \textit{Global Catastrophic Forgetting}, which corresponds to the fact that when the global model is trained on new tasks, its performance on old tasks decreases. There have been a few recent works on CFL to propose methods that aim to address the global catastrophic forgetting problem. However, these works either have unrealistic assumptions on the availability of past data samples or violate the privacy principles of FL. We propose a novel method, Federated Orthogonal Training (FOT), to overcome these drawbacks and address the global catastrophic forgetting in CFL. Our algorithm extracts the global input subspace of each layer for old tasks and modifies the aggregated updates of new tasks such that they are orthogonal to the global principal subspace of old tasks for each layer. This decreases the interference between tasks, which is the main cause for forgetting. % Our method is almost computation-free on the client side and has negligible communication cost. We empirically show that FOT outperforms state-of-the-art continual learning methods in the CFL setting, achieving an average accuracy gain of up to 15% with 27% lower forgetting while only incurring a minimal computation and communication cost. | [] | [] | Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning | [
"Yavuz Faruk Bakman",
"Duygu Nur Yaldiz",
"Yahya H. Ezzeldin",
"Salman Avestimehr"
] | 2309.01289 | 17,869 | https://openreview.net/forum?id=nAs4LdaP9Y |
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[] | Poster | [] | Long-term forecasting presents unique challenges due to the time and memorycomplexity of handling long sequences. Existing methods, which rely on sliding windows to process long sequences, struggle to effectively capture long-term variations that are partially caught within the short window (i.e., outer-window variations). In this paper, we introduce a novel approach that overcomes this limitation by employing contrastive learning and enhanced decomposition architecture,specifically designed to focus on long-term variations. To this end, our contrastiveloss incorporates global autocorrelation held in the whole time series, which facilitates the construction of positive and negative pairs in a self-supervised manner. When combined with our decomposition networks, our constrative learning significantly improves long-term forecasting performance. Extensive experiments demonstrate that our approach outperforms 14 baseline models on well-establishednine long-term benchmarks, especially in challenging scenarios that require a significantly long output for forecasting. This paper not only presents a novel direction for long-term forecasting but also offers a more reliable method for effectively integrating long-term variations into time-series representation learning. | [] | [] | Self-Supervised Contrastive Learning for Long-term Forecasting | [
"Junwoo Park",
"Daehoon Gwak",
"Jaegul Choo",
"Edward Choi"
] | 2402.02023 | 17,868 | https://openreview.net/forum?id=nBCuRzjqK7 |
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[] | Poster | [] | Federated optimization studies the problem of collaborative function optimization among multiple clients (e.g. mobile devices or organizations) under the coordination of a central server. Since the data is collected separately by each client and always remains decentralized, federated optimization preserves data privacy and allows for large-scale computing, which makes it a promising decentralized machine learning paradigm. Though it is often deployed for tasks that are online in nature, e.g., next-word prediction on keyboard apps, most works formulate it as an offline problem. The few exceptions that consider federated bandit optimization are limited to very simplistic function classes, e.g., linear, generalized linear, or non-parametric function class with bounded RKHS norm, which severely hinders its practical usage. In this paper, we propose a new algorithm, named Fed-GO-UCB, for federated bandit optimization with generic non-linear objective function. Under some mild conditions, we rigorously prove that Fed-GO-UCB is able to achieve sub-linear rate for both cumulative regret and communication cost. At the heart of our theoretical analysis are distributed regression oracle and individual confidence set construction, which can be of independent interests. Empirical evaluations also demonstrate the effectiveness of the proposed algorithm. | [] | [] | Communication-Efficient Federated Non-Linear Bandit Optimization | [
"Chuanhao Li",
"Chong Liu",
"Yu-Xiang Wang"
] | 2311.01695 | 17,866 | https://openreview.net/forum?id=nFI3wFM9yN |
|
[] | Poster | [] | Conventional causal discovery approaches, which seek to uncover causal relationships among measured variables, are typically fragile to the presence of latent variables. While various methods have been developed to address this confounding issue, they often rely on strong assumptions about the underlying causal structure. In this paper, we consider a general scenario where measured and latent variables collectively form a partially observed causally sufficient linear system and latent variables may be anywhere in the causal structure. We theoretically show that with the aid of high-order statistics, the causal graph is (almost) fully identifiable if, roughly speaking, each latent set has a sufficient number of pure children, which can be either latent or measured. Naturally, LiNGAM, a model without latent variables, is encompassed as a special case. Based on the identification theorem, we develop a principled algorithm to identify the causal graph by testing for statistical independence involving only measured variables in specific manners. Experimental results show that our method effectively recovers the causal structure, even when latent variables are influenced by measured variables. | [] | [] | Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability | [
"Songyao Jin",
"Feng Xie",
"Guangyi Chen",
"Biwei Huang",
"Zhengming Chen",
"Xinshuai Dong",
"Kun Zhang"
] | 17,863 | https://openreview.net/forum?id=nHkMm0ywWm |
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[] | Poster | [] | Existing analyses of the expressive capacity of Transformer models have required excessively deep layers for data memorization, leading to a discrepancy with the Transformers actually used in practice. This is primarily due to the interpretation of the softmax function as an approximation of the hardmax function.By clarifying the connection between the softmax function and the Boltzmann operator, we prove that a single layer of self-attention with low-rank weight matrices possesses the capability to perfectly capture the context of an entire input sequence.As a consequence, we show that one-layer and single-head Transformers have a memorization capacity for finite samples, and that Transformers consisting of one self-attention layer with two feed-forward neural networks are universal approximators for continuous functions on a compact domain. | [] | [] | Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators? | [
"Tokio Kajitsuka",
"Issei Sato"
] | 2307.14023 | 17,862 | https://openreview.net/forum?id=nJnky5K944 |
|
[] | Spotlight Poster | [] | Deep Ensembles (DEs) demonstrate improved accuracy, calibration and robustness to perturbations over single neural networks partly due to their functional diversity. Particle-based variational inference (ParVI) methods enhance diversity by formalizing a repulsion term based on a network similarity kernel. However, weight-space repulsion is inefficient due to over-parameterization, while direct function-space repulsion has been found to produce little improvement over DEs. To sidestep these difficulties, we propose First-order Repulsive Deep Ensemble (FoRDE), an ensemble learning method based on ParVI, which performs repulsion in the space of first-order input gradients. As input gradients uniquely characterize a function up to translation and are much smaller in dimension than the weights, this method guarantees that ensemble members are functionally different. Intuitively, diversifying the input gradients encourages each network to learn different features, which is expected to improve the robustness of an ensemble. Experiments on image classification datasets and transfer learning tasks show that FoRDE significantly outperforms the gold-standard DEs and other ensemble methods in accuracy and calibration under covariate shift due to input perturbations. | [] | [] | Input-gradient space particle inference for neural network ensembles | [
"Trung Trinh",
"Markus Heinonen",
"Luigi Acerbi",
"Samuel Kaski"
] | 2306.02775 | 17,861 | https://openreview.net/forum?id=nLWiR5P3wr |
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[] | Poster | [] | Integrated circuits or chips are key to enable computing in modern industry. Designing a chip relies on human experts to produce chip data through professional electronic design automation (EDA) software and complicated procedures. Nowadays, prompted by the wide variety of machine learning (ML) datasets, we have witnessed great advancement of ML algorithms in computer vision, natural language processing, and other fields. However, in chip design, high human workload and data sensitivity cause the lack of public datasets, which hinders the progress of ML development for EDA. To this end, we introduce an advanced large-scale dataset, CircuitNet 2.0, which targets promoting ML innovations in a realistic chip design environment. In order to approach the realistic chip design space, we collect more than 10,000 samples with a variety of chip designs (e.g., CPU, GPU, and AI Chip). All the designs are conducted through complete commercial design flows in a widely-used technology node, 14nm FinFET. We collect comprehensive data, including routability, timing, and power, from the design flow to support versatile ML tasks in EDA. Besides, we also introduce some realistic ML tasks with CircuitNet 2.0 to verify the potential for boosting innovations. | [] | [] | CircuitNet 2.0: An Advanced Dataset for Promoting Machine Learning Innovations in Realistic Chip Design Environment | [
"Xun Jiang",
"zhuomin chai",
"Yuxiang Zhao",
"Yibo Lin",
"Runsheng Wang",
"Ru Huang"
] | 17,860 | https://openreview.net/forum?id=nMFSUjxMIl |
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[] | Poster | [] | Federated Learning (FL) is a distributed machine learning technique where multiple devices (such as smartphones or IoT devices) train a shared global model by using their local data. FL claims that the data privacy of local participants is preserved well because local data will not be shared with either the server-side or other training participants. However, this paper discovers a pioneering finding that a model inversion (MI) attacker, who acts as a benign participant, can invert the shared global model and obtain the data belonging to other participants. This will lead to severe data-leakage risk in FL because it is difficult to identify attackers from benign participants.In addition, we found even the most advanced defense approaches could not effectively address this issue. Therefore, it is important to evaluate such data-leakage risks of an FL system before using it. To alleviate this issue, we propose FedInverse to evaluate whether the FL global model can be inverted by MI attackers. In particular, FedInverse can be optimized by leveraging the Hilbert-Schmidt independence criterion (HSIC) as a regularizer to adjust the diversity of the MI attack generator. We test FedInverse with three typical MI attackers, GMI, KED-MI, and VMI, and the experiments show our FedInverse method can successfully obtain the data belonging to other participants. | [] | [] | FedInverse: Evaluating Privacy Leakage in Federated Learning | [
"Di Wu",
"Jun Bai",
"Yiliao Song",
"Junjun Chen",
"Wei Zhou",
"Yong Xiang",
"Atul Sajjanhar"
] | 17,858 | https://openreview.net/forum?id=nTNgkEIfeb |
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[] | Poster | [] | The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a typical approach to distinguish non-isomorphic graphs, yet rarely adopted when developing expressive GNNs. This paper proposes to maximize the expressivity of GNNs by graph canonization, then the power of such GNNs is studies from the perspective of model stability. A stable GNN will map similar graphs to close graph representations in the vectorial space, and the stability of GNNs is critical to generalize their performance to unseen graphs. We theoretically reveal the trade-off of expressivity and stability in graph-canonization-enhanced GNNs. Then we introduce a notion of universal graph canonization as the general solution to address the trade-off and characterize a widely applicable sufficient condition to solve the universal graph canonization. A comprehensive set of experiments demonstrates the effectiveness of the proposed method. In many popular graph benchmark datasets, graph canonization successfully enhances GNNs and provides highly competitive performance, indicating the capability and great potential of proposed method in general graph representation learning. In graph datasets where the sufficient condition holds, GNNs enhanced by universal graph canonization consistently outperform GNN baselines and successfully improve the SOTA performance up to $31$%, providing the optimal solution to numerous challenging real-world graph analytical tasks like gene network representation learning in bioinformatics. | [] | [] | Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability | [
"Zehao Dong",
"Muhan Zhang",
"Philip Payne",
"Michael A Province",
"Carlos Cruchaga",
"Tianyu Zhao",
"Fuhai Li",
"Yixin Chen"
] | 2309.00738 | 17,857 | https://openreview.net/forum?id=nTwb2vBLOV |
|
[] | Poster | [] | A critical barrier to learning an accurate decision rule for outlier detection is the scarcity of outlier data. As such, practitioners often turn to the use of similar but imperfect outlier data from which they might \emph{transfer} information to the target outlier detection task. Despite the recent empirical success of transfer learning in outlier detection, a fundamental understanding of when and how knowledge can be transferred from a source to a target in outlier detection remains elusive. In this work, we adopt the traditional framework of Neyman-Pearson classification---which formalizes \emph{supervised outlier detection}, i.e., unbalanced classification---with the added assumption that we have access to both source and (some or no) target outlier data. Our main results are then as follows:We first determine the information-theoretic limits of the problem under a measure of discrepancy that extends some existing notions from traditional balanced classification; interestingly, unlike in balanced classification, seemingly very dissimilar sources can provide much information about a target, thus resulting in fast transfer.We then show that, in principle, these information-theoretic limits are achievable by \emph{adaptive} procedures, i.e., procedures with no a priori information on the discrepancy between source and target distributions. | [] | [] | Tight Rates in Supervised Outlier Transfer Learning | [
"Mohammadreza Mousavi Kalan",
"Samory Kpotufe"
] | 2310.04686 | 17,856 | https://openreview.net/forum?id=nUBLhhVM1l |
|
[] | Poster | [
"https://github.com/thuiar/MIntRec2.0"
] | Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. However, most existing multimodal intent benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-turn conversational interactions. In this paper, we introduce MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 high-quality dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes, across text, video, and audio modalities. In addition to more than 9,300 in-scope samples, it also includes over 5,700 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world open scenarios, enhancing its practical applicability. Furthermore, we provide comprehensive information on the speakers in each utterance, enriching its utility for multi-party conversational research. We establish a general framework supporting the organization of single-turn and multi-turn dialogue data, modality feature extraction, multimodal fusion, as well as in-scope classification and out-of-scope detection. Evaluation benchmarks are built using classic multimodal fusion methods, ChatGPT, and human evaluators. While existing methods incorporating nonverbal information yield improvements, effectively leveraging context information and detecting out-of-scope samples remains a substantial challenge. Notably, powerful large language models exhibit a significant performance gap compared to humans, highlighting the limitations of machine learning methods in the advanced cognitive intent understanding task. We believe that MIntRec2.0 will serve as a valuable resource, providing a pioneering foundation for research in human-machine conversational interactions, and significantly facilitating related applications. | [] | [] | MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations | [
"Hanlei Zhang",
"Xin Wang",
"Hua Xu",
"Qianrui Zhou",
"Kai Gao",
"Jianhua Su",
"jinyue Zhao",
"Wenrui Li",
"Yanting Chen"
] | 2403.10943 | 17,855 | https://openreview.net/forum?id=nY9nITZQjc |
|
[] | Poster | [] | Adversarial patch attacks, where a small patch is placed in the scene to fool neural networks, have been studied for numerous applications. Focusing on image classification, we consider the setting of a black-box transfer attack where an attacker does not know the target model. Instead of forcing corrupted image representations to cross the nearest decision boundaries or converge to a particular point, we propose a distribution-oriented approach. We rely on optimal transport to push the feature distribution of attacked images towards an already modeled distribution. We show that this new distribution-oriented approach leads to better transferable patches. Through digital experiments conducted on ImageNet-1K, we provide evidence that our new patches are the only ones that can simultaneously influence multiple Transformer models and Convolutional Neural Networks. Physical world experiments demonstrate that our patch can affect systems in deployment without explicit knowledge. | [] | [] | Optimal transport based adversarial patch to leverage large scale attack transferability | [
"Pol Labarbarie",
"Adrien CHAN-HON-TONG",
"Stéphane Herbin",
"Milad Leyli-abadi"
] | 17,854 | https://openreview.net/forum?id=nZP10evtkV |
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[] | Poster | [] | Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously. A recent development known as task arithmetic has revealed that several models, each fine-tuned for distinct tasks, can be directly merged into a single model to execute MTL without necessitating a retraining process using the initial training data. Nevertheless, this direct addition of models often leads to a significant deterioration in the overall performance of the merged model. This decline occurs due to potential conflicts and intricate correlations among the multiple tasks. Consequently, the challenge emerges of how to merge pre-trained models more effectively without using their original training data. This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging). This approach aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data. Specifically, our AdaMerging method operates as an automatic, unsupervised task arithmetic scheme. It leverages entropy minimization on unlabeled test samples from the multi-task setup as a surrogate objective function to iteratively refine the merging coefficients of the multiple models. Our experimental findings across eight tasks demonstrate the efficacy of the AdaMerging scheme we put forth. Compared to the current state-of-the-art (SOTA) task arithmetic merging scheme, AdaMerging showcases a remarkable 11\% improvement in performance. Notably, AdaMerging also exhibits superior generalization capabilities when applied to unseen downstream tasks. Furthermore, it displays a significantly enhanced robustness to data distribution shifts that may occur during the testing phase. | [] | [] | AdaMerging: Adaptive Model Merging for Multi-Task Learning | [
"Enneng Yang",
"Zhenyi Wang",
"Li Shen",
"Shiwei Liu",
"Guibing Guo",
"Xingwei Wang",
"Dacheng Tao"
] | 2310.02575 | 17,853 | https://openreview.net/forum?id=nZP6NgD3QY |
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[] | Poster | [] | Out-of-distribution (OOD) detection is indispensable for open-world machine learning models. Inspired by recent success in large pre-trained language-vision models, e.g., CLIP, advanced works have achieved impressive OOD detection results by matching the *similarity* between image features and features of learned prompts, i.e., positive prompts. However, existing works typically struggle with OOD samples having similar features with those of known classes. One straightforward approach is to introduce negative prompts to achieve a *dissimilarity* matching, which further assesses the anomaly level of image features by introducing the absence of specific features. Unfortunately, our experimental observations show that either employing a prompt like "not a photo of a" or learning a prompt to represent "not containing" fails to capture the dissimilarity for identifying OOD samples. The failure may be contributed to the diversity of negative features, i.e., tons of features could indicate features not belonging to a known class. To this end, we propose to learn a set of negative prompts for each class. The learned positive prompt (for all classes) and negative prompts (for each class) are leveraged to measure the similarity and dissimilarity in the feature space simultaneously, enabling more accurate detection of OOD samples. Extensive experiments are conducted on diverse OOD detection benchmarks, showing the effectiveness of our proposed method. | [] | [] | Out-of-Distribution Detection with Negative Prompts | [
"Jun Nie",
"Yonggang Zhang",
"Zhen Fang",
"Tongliang Liu",
"Bo Han",
"Xinmei Tian"
] | 17,852 | https://openreview.net/forum?id=nanyAujl6e |
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[] | Poster | [] | In Federated Learning (FL), model aggregation is pivotal. It involves a global server iteratively aggregating client local trained models in successive rounds without accessing private data. Traditional methods typically aggregate the local model from the current round alone. However, due to the statistical heterogeneity across clients, the local model from each client may be greatly diverse, making the obtained global model incapable of maintaining their specific knowledge. In this paper, we introduce a novel method, FedCDA, which selectively aggregates local models from various rounds, decreasing discrepancies between local models. The principle behind FedCDA is that the local model from each client may converge to distinct local optima over rounds due to the varied received global models and non-convex essences of deep neural networks, and each local model fits its local data well. Therefore, for each client, we select a local model from multiple rounds to minimize the divergence from other clients. This ensures the aggregated global model remains aligned with all selected local models to maintain their data knowledge. Extensive experiments conducted on various models and datasets reveal our approach outperforms state-of-the-art aggregation methods. | [] | [] | FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation | [
"Haozhao Wang",
"Haoran Xu",
"Yichen Li",
"Yuan Xu",
"Ruixuan Li",
"Tianwei Zhang"
] | 17,851 | https://openreview.net/forum?id=nbPGqeH3lt |
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[] | Spotlight Poster | [] | Different from traditional task-specific vision models, recent large VLMs can readily adapt to different vision tasks by simply using different textual instructions, i.e., prompts. However, a well-known concern about traditional task-specific vision models is that they can be misled by imperceptible adversarial perturbations. Furthermore, the concern is exacerbated by the phenomenon that the same adversarial perturbations can fool different task-specific models. Given that VLMs rely on prompts to adapt to different tasks, an intriguing question emerges: Can a single adversarial image mislead all predictions of VLMs when a thousand different prompts are given? This question essentially introduces a novel perspective on adversarial transferability: cross-prompt adversarial transferability. In this work, we propose the Cross-Prompt Attack (CroPA). This proposed method updates the visual adversarial perturbation with learnable textual prompts, which are designed to counteract the misleading effects of the adversarial image. By doing this, CroPA significantly improves the transferability of adversarial examples across prompts. Extensive experiments are conducted to verify the strong cross-prompt adversarial transferability of CroPA with prevalent VLMs including Flamingo, BLIP-2, and InstructBLIP in various different tasks. | [] | [] | An Image Is Worth 1000 Lies: Transferability of Adversarial Images across Prompts on Vision-Language Models | [
"Haochen Luo",
"Jindong Gu",
"Fengyuan Liu",
"Philip Torr"
] | 17,850 | https://openreview.net/forum?id=nc5GgFAvtk |
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[] | Poster | [] | We study the problem of training a flow-based generative model, parametrized by a two-layer autoencoder, to sample from a high-dimensional Gaussian mixture. We provide a sharp end-to-end analysis of the problem. First, we provide a tight closed-form characterization of the learnt velocity field, when parametrized by a shallow denoising auto-encoder trained on a finite number $n$ of samples from the target distribution. Building on this analysis, we provide a sharp description of the corresponding generative flow, which pushes the base Gaussian density forward to an approximation of the target density. In particular, we provide closed-form formulae for the distance between the means of the generated mixture and the mean of the target mixture, which we show decays as $\Theta_n(\frac{1}{n})$. Finally, this rate is shown to be in fact Bayes-optimal. | [] | [] | Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity | [
"Hugo Cui",
"Florent Krzakala",
"Eric Vanden-Eijnden",
"Lenka Zdeborova"
] | 2310.03575 | 17,849 | https://openreview.net/forum?id=ndCJeysCPe |
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[] | Poster | [] | Self-consistency (SC) has been a widely used decoding strategy for chain-of-thought reasoning. Despite bringing significant performance improvements across a variety of multi-step reasoning tasks, it is a high-cost method that requires multiple sampling with the preset size. In this paper, we propose a simple and scalable sampling process, Early-Stopping Self-Consistency (ESC), to greatly reduce the cost of SC without sacrificing performance. On this basis, one control scheme for ESC is further derivated to dynamically choose the performance-cost balance for different tasks and models. To demonstrate ESC's effectiveness, we conducted extensive experiments on three popular categories of reasoning tasks: arithmetic, commonsense and symbolic reasoning over language models with varying scales. The empirical results show that ESC reduces the average number of sampling of chain-of-thought reasoning by a significant margin on six benchmarks, including MATH (-33.8%), GSM8K (-80.1%), StrategyQA (-76.8%), CommonsenseQA (-78.5%), Coin Flip (-84.2%) and Last Letters (-67.4%), while attaining comparable performances. | [] | [] | Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning | [
"Yiwei Li",
"Peiwen Yuan",
"Shaoxiong Feng",
"Boyuan Pan",
"Xinglin Wang",
"Bin Sun",
"Heda Wang",
"Kan Li"
] | 2401.10480 | 17,848 | https://openreview.net/forum?id=ndR8Ytrzhh |
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[] | Poster | [] | Graph Neural Networks (GNNs) have emerged as the leading deep learning models for graph-based representation learning. However, the training and inference of GNNs on large graphs remain resource-intensive, impeding their utility in real-world scenarios and curtailing their applicability in deeper and more sophisticated GNN architectures. To address this issue, the Graph Lottery Ticket (GLT) hypothesis assumes that GNN with random initialization harbors a pair of core subgraph and sparse subnetwork, which can yield comparable performance and higher efficiency to that of the original dense network and complete graph. Despite that GLT offers a new paradigm for GNN training and inference, existing GLT algorithms heavily rely on trial-and-error pruning rate tuning and scheduling, and adhere to an irreversible pruning paradigm that lacks elasticity. Worse still, current methods suffer scalability issues when applied to deep GNNs, as they maintain the same topology structure across all layers. These challenges hinder the integration of GLT into deeper and larger-scale GNN contexts. To bridge this critical gap, this paper introduces an $\textbf{A}$daptive, $\textbf{D}$ynamic, and $\textbf{A}$utomated framework for identifying $\textbf{G}$raph $\textbf{L}$ottery $\textbf{T}$ickets ($\textbf{AdaGLT}$). Our proposed method derives its key advantages and addresses the above limitations through the following three aspects: 1) tailoring layer-adaptive sparse structures for various datasets and GNNs, thus endowing it with the capability to facilitate deeper GNNs; 2) integrating the pruning and training processes, thereby achieving a dynamic workflow encompassing both pruning and restoration; 3) automatically capturing graph lottery tickets across diverse sparsity levels, obviating the necessity for extensive pruning parameter tuning. More importantly, we rigorously provide theoretical proofs to guarantee $\textbf{AdaGLT}$ to mitigate over-smoothing issues and obtain improved sparse structures in deep GNN scenarios. Extensive experiments demonstrate that $\textbf{AdaGLT}$ outperforms state-of-the-art competitors across multiple graph datasets of various scales and types, particularly in scenarios involving deep GNNs. | [] | [] | Graph Lottery Ticket Automated | [
"Guibin Zhang",
"Kun Wang",
"Wei Huang",
"Yanwei Yue",
"Yang Wang",
"Roger Zimmermann",
"Aojun Zhou",
"Dawei Cheng",
"Jin Zeng",
"Yuxuan Liang"
] | 17,845 | https://openreview.net/forum?id=nmBjBZoySX |
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[] | Poster | [] | Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning. These issues could be partially addressed by introducing external knowledge graphs (KG) in LLM reasoning. In this paper, we propose a new LLM-KG integrating paradigm ``$\hbox{LLM}\otimes\hbox{KG}$'' which treats the LLM as an agent to interactively explore related entities and relations on KGs and perform reasoning based on the retrieved knowledge. We further implement this paradigm by introducing a new approach called Think-on-Graph (ToG), in which the LLM agent iteratively executes beam search on KG, discovers the most promising reasoning paths, and returns the most likely reasoning results. We use a number of well-designed experiments to examine and illustrate the following advantages of ToG: 1) compared with LLMs, ToG has better deep reasoning power; 2) ToG has the ability of knowledge traceability and knowledge correctability by leveraging LLMs reasoning and expert feedback; 3) ToG provides a flexible plug-and-play framework for different LLMs, KGs and prompting strategies without any additional training cost; 4) the performance of ToG with small LLM models could exceed large LLM such as GPT-4 in certain scenarios and this reduces the cost of LLM deployment and application. As a training-free method with lower computational cost and better generality, ToG achieves overall SOTA in 6 out of 9 datasets where most previous SOTAs rely on additional training. | [] | [] | Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph | [
"Jiashuo Sun",
"Chengjin Xu",
"Lumingyuan Tang",
"Saizhuo Wang",
"Chen Lin",
"Yeyun Gong",
"Lionel Ni",
"Heung-Yeung Shum",
"Jian Guo"
] | 2307.07697 | 17,844 | https://openreview.net/forum?id=nnVO1PvbTv |
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[] | Poster | [] | Machine learning methods can be a valuable aid in the scientific process, but they need to face challenging settings where data come from inhomogeneous experimental conditions. Recent meta-learning methods have made significant progress in multi-task learning, but they rely on black-box neural networks, resulting in high computational costs and limited interpretability. We introduce CAMEL, a new meta-learning architecture capable of learning efficiently from multiple environments, with an affine structure with respect to the learning task. We prove that CAMEL can identify the physical parameters of the system, enabling interpreable learning. We demonstrate the competitive generalization performance and the low computational cost of our method by comparing it to state-of-the-art algorithms on physical systems, ranging from toy models to complex, non-analytical systems. The interpretability of our method is illustrated with original applications to physical-parameter-induced adaptation and to adaptive control and system identification. | [] | [] | Interpretable Meta-Learning of Physical Systems | [
"Matthieu Blanke",
"Marc Lelarge"
] | 2312.00477 | 17,843 | https://openreview.net/forum?id=nnicaG5xiH |
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[] | Spotlight Poster | [] | We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating the camera poses in 1.3 seconds on a single A100 GPU. PF-LRM is a highly scalable method utilizing the self-attention blocks to exchange information between 3D object tokens and 2D image tokens; we predict coarse geometry for each view, and then use a differentiable Perspective-n-Point (PnP) solver to obtain camera poses. When trained on a huge amount of multi-view data, PF-LRM shows strong cross-dataset generalization ability, and outperforms baseline methods by a large margin in terms of pose prediction accuracy and 3D reconstruction quality on various evaluation datasets. We also demonstrate our model's robustness to variable numbers of input views and segmentation mask errors. Our project website is at: https://pf-lrm.github.io/project. | [] | [] | PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction | [
"Peng Wang",
"Hao Tan",
"Sai Bi",
"Yinghao Xu",
"Fujun Luan",
"Kalyan Sunkavalli",
"Wenping Wang",
"Zexiang Xu",
"Kai Zhang"
] | 17,842 | https://openreview.net/forum?id=noe76eRcPC |
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[] | Poster | [] | Binary classification typically involves predicting the label of an instance based on whether the model score for the positive class exceeds a threshold chosen based on the application requirements (e.g., maximizing recall for a precision bound). However, model scores are often not aligned with true positivity rate. This is especially true when the training involves a differential sampling of classes or there is distributional drift between train and test settings. In this paper, we provide theoretical analysis and empirical evidence of the dependence of estimation bias on both uncertainty and model score. Further, we formulate the decision boundary selection using both model score and uncertainty, prove that it is NP-hard, and present algorithms based on dynamic programming and isotonic regression. Evaluation of the proposed algorithms on three real-world datasets yield 25\%-40\% improvement in recall at high precision bounds over the traditional approach of using model score alone, highlighting the benefits of leveraging uncertainty. | [] | [] | Leveraging Uncertainty Estimates To Improve Classifier Performance | [
"Gundeep Arora",
"Srujana Merugu",
"Anoop Saladi",
"Rajeev Rastogi"
] | 2311.11723 | 17,840 | https://openreview.net/forum?id=nsNyDvNQTc |
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[] | Poster | [] | We consider the problem of linear estimation, and establish an extension of the Gauss-Markov theorem, in which the bias operator is allowed to be non-zero but bounded with respect to a matrix norm of Schatten type. We derive simple and explicit formulas for the optimal estimator in the cases of Nuclear and Spectral norms (with the Frobenius case recovering ridge regression). Additionally, we analytically derive the generalization error in multiple random matrix ensembles, and compare with Ridge regression. Finally, we conduct an extensive simulation study, in which we show that the cross-validated Nuclear and Spectral regressors can outperform Ridge in several circumstances. | [] | [] | Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem | [
"Simon Segert"
] | 2311.11093 | 17,839 | https://openreview.net/forum?id=nxnbPPVvOG |
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[] | Poster | [] | This paper introduces $\pi$2vec, a method for representing black box policies as comparable feature vectors.Our method combines the strengths of foundation models that serve as generic and powerful state representations and successor features that can model the future occurrence of the states for a policy.$\pi$2vec represents the behavior of policies by capturing the statistics of the features from a pretrained model with the help of successor feature framework. We focus on the offline setting where policies and their representations are trained on a fixed dataset of trajectories.Finally, we employ linear regression on $\pi$2vec vector representations to predict the performance of held out policies.The synergy of these techniques results in a method for efficient policy evaluation in resource constrained environments. | [] | [] | $\pi$2vec: Policy Representation with Successor Features | [
"Gianluca Scarpellini",
"Ksenia Konyushkova",
"Claudio Fantacci",
"Thomas Paine",
"Yutian Chen",
"Misha Denil"
] | 2306.09800 | 17,835 | https://openreview.net/forum?id=o5Bqa4o5Mi |
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[] | Poster | [] | We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under general function approximation. In order to find the minimum assumption for sample-efficient learning, we introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs. Using this measure, we propose the first unified algorithmic framework that ensures sample efficiency in learning Nash Equilibrium, Coarse Correlated Equilibrium, and Correlated Equilibrium for both model-based and model-free MARL problems with low MADC. We also show that our algorithm provides comparable sublinear regret to the existing works. Moreover, our algorithm combines an equilibrium-solving oracle with a single objective optimization subprocedure that solves for the regularized payoff of each deterministic joint policy, which avoids solving constrained optimization problems within data-dependent constraints (Jin et al. 2020; Wang et al. 2023) or executing sampling procedures with complex multi-objective optimization problems (Foster et al. 2023), thus being more amenable to empirical implementation. | [] | [] | Sample-Efficient Multi-Agent RL: An Optimization Perspective | [
"Nuoya Xiong",
"Zhihan Liu",
"Zhaoran Wang",
"Zhuoran Yang"
] | 2310.06243 | 17,834 | https://openreview.net/forum?id=o7qhUMylLU |
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[] | Poster | [] | Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitigate this, existing methods incorporate the extra cue as guidance for image completion. Despite improvements, these approaches are often restricted to employing a *single modality* (e.g., *segmentation* or *sketch* maps), which lacks scalability in leveraging multi-modality for more plausible completion.In this paper, we propose a novel, simple yet effective method for **M**ulti-mod**a**l **G**uided **I**mage **C**ompletion, dubbed **MaGIC**, which not only supports a wide range of single modality as the guidance (e.g., *text*, *canny edge*, *sketch*, *segmentation*, *depth*, and *pose*), but also adapts to arbitrarily customized combinations of these modalities (i.e., *arbitrary multi-modality*) for image completion.For building MaGIC, we first introduce a modality-specific conditional U-Net (MCU-Net) that injects single-modal signal into a U-Net denoiser for single-modal guided image completion. Then, we devise a consistent modality blending (CMB) method to leverage modality signals encoded in multiple learned MCU-Nets through gradient guidance in latent space. Our CMB is *training-free*, thereby avoids the cumbersome joint re-training of different modalities, which is the secret of MaGIC to achieve exceptional flexibility in accommodating new modalities for completion.Experiments show the superiority of MaGIC over state-of-the-art methods and its generalization to various completion tasks. | [] | [] | MaGIC: Multi-modality Guided Image Completion | [
"Hao Wang",
"Yongsheng Yu",
"Tiejian Luo",
"Heng Fan",
"Libo Zhang"
] | 2305.11818 | 17,833 | https://openreview.net/forum?id=o7x0XVlCpX |
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[] | Spotlight Poster | [] | The information bottleneck principle provides an information-theoretic method for learning a good representation as a tradeoff between conciseness and predictive ability, which can reduce the information redundancy, eliminate irrelevant and superfluous features, and thus enhance the in-domain generalizability. However, in low-resource or out-of-domain scenarios where the assumption of iid does not necessarily hold true, superfluous (or redundant) relevant features may be supplemental to the mainline features of the model, and be beneficial in making prediction for test dataset with distribution shifts. To address this problem, we propose to keep as much relevant information as possible in use for making predictions. A three-stage supervised learning framework is designed and implemented to jointly learn the mainline and supplemental features, relieving supplemental features from the suppression of mainline features. Experiments on image and text classification tasks have shown our method substantially outperforms several baseline and state-of-the-art methods, especially in low resource cases. | [] | [] | Information Retention via Learning Supplemental Features | [
"Zhipeng Xie",
"Yahe Li"
] | 17,832 | https://openreview.net/forum?id=o83eu4H9Mb |
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[] | Spotlight Poster | [
"https://github.com/JinXins/Adversarial-AutoMixup"
] | Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and saliency information based mixup, has been gradually replaced by automatic mixing approaches. Through minimizing two sub-tasks, namely, mixed sample generation and mixup classification in an end-to-end way, AutoMix significantly improves accuracy on image classification tasks. However, as the optimization objective is consistent for the two sub-tasks, this approach is prone to generating consistent instead of diverse mixed samples, which results in overfitting for target task training. In this paper, we propose AdAutomixup, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust vein classifier for palm-vein identification, by alternatively optimizing the classifier and the mixup sample generator. AdAutomixup comprises two modules, a mixed example generator and a target classifier. The mixed sample generator aims to produce hard mixed examples to challenge the target classifier while the target classifier's aim is to learn robust features from hard mixed examples to improve generalization. To prevent the collapse of the inherent meanings of images, we further introduce an exponential moving average (EMA) teacher and a cosine similarity to train AdAutomixup in an end-to-end way. Extensive experiments on five image benchmarks consistently prove that our approach outperforms the state-of-the-art in various classification scenarios. | [] | [] | Adversarial AutoMixup | [
"Huafeng Qin",
"Xin Jin",
"Yun Jiang",
"Mounîm El-Yacoubi",
"Xinbo Gao"
] | 2312.11954 | 17,831 | https://openreview.net/forum?id=o8tjamaJ80 |
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[] | Poster | [] | There is a long history, as well as a recent explosion of interest, in statistical and generative modeling approaches based on \emph{score functions} --- derivatives of the log-likelihood of a distribution. In seminal works, Hyv\"arinen proposed vanilla score matching as a way to learn distributions from data by computing an estimate of the score function of the underlying ground truth, and established connections between this method and established techniques like Contrastive Divergence and Pseudolikelihood estimation. It is by now well-known that vanilla score matching has significant difficulties learning multimodal distributions. Although there are various ways to overcome this difficulty, the following question has remained unanswered --- is there a natural way to sample multimodal distributions using just the vanilla score? Inspired by a long line of related experimental works, we prove that the Langevin diffusion with early stopping, initialized at the empirical distribution, and run on a score function estimated from data successfully generates natural multimodal distributions (mixtures of log-concave distributions). | [] | [] | Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization | [
"Frederic Koehler",
"Thuy-Duong Vuong"
] | 2310.01762 | 17,830 | https://openreview.net/forum?id=oAMArMMQxb |
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[] | Poster | [] | We introduce Transformer-VQ, a decoder-only transformer computing softmax-based dense self-attention in linear time. Transformer-VQ's efficient attention is enabled by vector-quantized keys and a novel caching mechanism. In large-scale experiments, Transformer-VQ is shown highly competitive in quality, with strong results on Enwik8 (0.99 bpb), PG-19 (26.6 ppl), and ImageNet64 (3.16 bpb). Code: https://github.com/transformer-vq/transformer_vq | [] | [] | Transformer-VQ: Linear-Time Transformers via Vector Quantization | [
"Lucas Dax Lingle"
] | 17,829 | https://openreview.net/forum?id=oDdzXQzP2F |
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[] | Poster | [
"https://github.com/zeyuliu1037/LMUFormer.git"
] | Transformer models have demonstrated high accuracy in numerous applications but have high complexity and lack sequential processing capability making them ill-suited for many streaming applications at the edge where devices are heavily resource-constrained. Thus motivated, many researchers have proposed reformulating the transformer models as RNN modules which modify the self-attention computation with explicit states. However, these approaches often incur significant performance degradation.The ultimate goal is to develop a model that has the following properties: parallel training, streaming and low-cost inference, and state-of-the-art (SOTA) performance. In this paper, we propose a new direction to achieve this goal. We show how architectural modifications to a fully-sequential recurrent model can help push its performance toward Transformer models while retaining its sequential processing capability. Specifically, inspired by the recent success of Legendre Memory Units (LMU) in sequence learning tasks, we propose LMUFormer, which augments the LMU with convolutional patch embedding and convolutional channel mixer. Moreover, we present a spiking version of this architecture, which introduces the benefit of states within the patch embedding and channel mixer modules while simultaneously reducing the computing complexity. We evaluated our architectures on multiple sequence datasets. Of particular note is our performance on the Speech Commands V2 dataset (35 classes). In comparison to SOTA transformer-based models within the ANN domain, our LMUFormer demonstrates comparable performance while necessitating a remarkable $70\times$ reduction in parameters and a substantial $140\times$ decrement in FLOPs. Furthermore, when benchmarked against extant low-complexity SNN variants, our model establishes a new SOTA with an accuracy of 96.12\%. Additionally, owing to our model's proficiency in real-time data processing, we are able to achieve a 32.03\% reduction in sequence length, all while incurring an inconsequential decline in performance. | [] | [] | LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units | [
"Zeyu Liu",
"Gourav Datta",
"Anni Li",
"Peter Anthony Beerel"
] | 2402.04882 | 17,828 | https://openreview.net/forum?id=oEF7qExD9F |
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[] | Poster | [] | With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and tested in simplified synthetic environments, leading to a disconnect with real-world scenarios. In this paper, we build an environment for language-guided agents that is highly realistic and reproducible. Specifically, we focus on agents that perform tasks on the web, and create an environment with fully functional websites from four common domains: e-commerce, social forum discussions, collaborative software development, and content management. Our environment is enriched with tools (e.g., a map) and external knowledge bases (e.g., user manuals) to encourage human-like task-solving. Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions. The tasks in our benchmark are diverse, long-horizon, and designed to emulate tasks that humans routinely perform on the internet. We experiment with several baseline agents, integrating recent techniques such as reasoning before acting. The results demonstrate that solving complex tasks is challenging: our best GPT-4-based agent only achieves an end-to-end task success rate of 14.41%, significantly lower than the human performance of 78.24%. These results highlight the need for further development of robust agents, that current state-of-the-art large language models are far from perfect performance in these real-life tasks, and that \ours can be used to measure such progress.\footnote{Code, data, environment reproduction instructions, video demonstrations are available in the supplementary.} | [] | [] | WebArena: A Realistic Web Environment for Building Autonomous Agents | [
"Shuyan Zhou",
"Frank F. Xu",
"Hao Zhu",
"Xuhui Zhou",
"Robert Lo",
"Abishek Sridhar",
"Xianyi Cheng",
"Tianyue Ou",
"Yonatan Bisk",
"Daniel Fried",
"Uri Alon",
"Graham Neubig"
] | 2307.13854 | 17,826 | https://openreview.net/forum?id=oKn9c6ytLx |
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[] | Poster | [] | Although reinforcement learning methods offer a powerful framework for automatic skill acquisition, for practical learning-based control problems in domains such as robotics, imitation learning often provides a more convenient and accessible alternative. In particular, an interactive imitation learning method such as DAgger, which queries a near-optimal expert to intervene online to collect correction data for addressing the distributional shift challenges that afflict naïve behavioral cloning, can enjoy good performance both in theory and practice without requiring manually specified reward functions and other components of full reinforcement learning methods. In this paper, we explore how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert. We also provide a unified framework to analyze our RL method and DAgger; for which we present the asymptotic analysis of the suboptimal gap for both methods as well as the non-asymptotic sample complexity bound of our method. We then evaluate our method on challenging high-dimensional continuous control simulation benchmarks as well as real-world robotic vision-based manipulation tasks. The results show that it strongly outperforms DAgger-like approaches across the different tasks, especially when the intervening experts are suboptimal. Additional ablations also empirically verify the proposed theoretical justification that the performance of our method is associated with the choice of intervention model and suboptimality of the expert. Code and videos can be found on our project website: https://rlifpaper.github.io. | [] | [] | RLIF: Interactive Imitation Learning as Reinforcement Learning | [
"Jianlan Luo",
"Perry Dong",
"Yuexiang Zhai",
"Yi Ma",
"Sergey Levine"
] | 2311.12996 | 17,825 | https://openreview.net/forum?id=oLLZhbBSOU |
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[] | Poster | [] | Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery. Current studies consider leveraging both 2D and 3D molecular structures for representation learning. However, relying on straightforward alignment strategies that treat each modality separately, these methods fail to exploit the intrinsic correlation between 2D and 3D representations that reflect the underlying structural characteristics of molecules, and only perform coarse-grained molecule-level alignment. To derive fine-grained alignment and promote structural molecule understanding, we introduce an atomic-relation level "blend-then-predict" self-supervised learning approach, MoleBLEND, which first blends atom relations represented by different modalities into one unified relation matrix for joint encoding, then recovers modality-specific information for 2D and 3D structures individually. By treating atom relationships as anchors, MoleBLEND organically aligns and integrates visually dissimilar 2D and 3D modalities of the same molecule at fine-grained atomic level, painting a more comprehensive depiction of each molecule. Extensive experiments show that MoleBLEND achieves state-of-the-art performance across major 2D/3D molecular benchmarks. We further provide theoretical insights from the perspective of mutual-information maximization, demonstrating that our method unifies contrastive, generative (cross-modality prediction) and mask-then-predict (single-modality prediction) objectives into one single cohesive framework. | [] | [] | Multimodal Molecular Pretraining via Modality Blending | [
"Qiying Yu",
"Yudi Zhang",
"Yuyan Ni",
"Shikun Feng",
"Yanyan Lan",
"Hao Zhou",
"Jingjing Liu"
] | 2307.06235 | 17,824 | https://openreview.net/forum?id=oM7Jbxdk6Z |
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[] | Poster | [] | Decoding the linguistic intricacies of the genome is a crucial problem in biology, and pre-trained foundational models such as DNABERT and Nucleotide Transformer have made significant strides in this area. Existing works have largely hinged on k-mer, fixed-length permutations of A, T, C, and G, as the token of the genome language due to its simplicity. However, we argue that the computation and sample inefficiencies introduced by k-mer tokenization are primary obstacles in developing large genome foundational models. We provide conceptual and empirical insights into genome tokenization, building on which we propose to replace k-mer tokenization with Byte Pair Encoding (BPE), a statistics-based data compression algorithm that constructs tokens by iteratively merging the most frequent co-occurring genome segment in the corpus. We demonstrate that BPE not only overcomes the limitations of k-mer tokenization but also benefits from the computational efficiency of non-overlapping tokenization.Based on these insights, we introduce DNABERT-2, a refined genome foundation model that adapts an efficient tokenizer and employs multiple strategies to overcome input length constraints, reduce time and memory expenditure, and enhance model capability. Furthermore, we identify the absence of a comprehensive and standardized benchmark for genome understanding as another significant impediment to fair comparative analysis. In response, we propose the Genome Understanding Evaluation (GUE), a comprehensive multi-species genome classification dataset that amalgamates $28$ distinct datasets across $7$ tasks, with input lengths ranging from $70$ to $1000$. Through comprehensive experiments on the GUE benchmark, we demonstrate that DNABERT-2 achieves comparable performance to the state-of-the-art model with $21 \times$ fewer parameters and approximately $92 \times$ less GPU time in pre-training. Compared to DNABERT, while being $3 \times$ more efficient, DNABERT-2 outperforms it on $23$ out of $28$ datasets, with an average improvement of $6$ absolute scores on GUE.The code, data, and pre-trained model will be publicly available. | [] | [] | DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes | [
"Zhihan Zhou",
"Yanrong Ji",
"Weijian Li",
"Pratik Dutta",
"Ramana V Davuluri",
"Han Liu"
] | 17,823 | https://openreview.net/forum?id=oMLQB4EZE1 |
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[] | Poster | [] | Bayesian optimization under contextual uncertainty (BOCU) is a family of BO problems in which the learner makes a decision prior to observing the context and must manage the risks involved. Distributionally robust BO (DRBO) is a subset of BOCU that affords robustness against context distribution shift, and includes the optimization of expected values and worst-case values as special cases. By considering the first derivatives of the DRBO objective, we generalize DRBO to one that includes several other uncertainty objectives studied in the BOCU literature such as worst-case sensitivity (and thus notions of risk such as variance, range, and conditional value-at-risk) and mean-risk tradeoffs. We develop a general Thompson sampling algorithm that is able to optimize any objective within the BOCU framework, analyze its theoretical properties, and compare it to suitable baselines across different experimental settings and uncertainty objectives. | [] | [] | A Unified Framework for Bayesian Optimization under Contextual Uncertainty | [
"Sebastian Shenghong Tay",
"Chuan-Sheng Foo",
"Daisuke Urano",
"Richalynn Leong",
"Bryan Kian Hsiang Low"
] | 17,822 | https://openreview.net/forum?id=oMNkj4ER7V |
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[] | Poster | [] | In observational studies, balancing covariates in different treatment groups is essential to estimate treatment effects. One of the most commonly used methods for such purposes is weighting. The performance of this class of methods usually depends on strong regularity conditions for the underlying model, which might not hold in practice. In this paper, we investigate weighting methods from a functional estimation perspective and argue that the weights needed for covariate balancing could differ from those needed for treatment effects estimation under low regularity conditions. Motivated by this observation, we introduce a new framework of weighting that directly targets the treatment effects estimation. Unlike existing methods, the resulting estimator for a treatment effect under this new framework is a simple kernel-based $U$-statistic after applying a data-driven transformation to the observed covariates. We characterize the theoretical properties of the new estimators of treatment effects under a nonparametric setting and show that they are able to work robustly under low regularity conditions. The new framework is also applied to several numerical examples to demonstrate its practical merits. | [] | [] | Treatment Effects Estimation By Uniform Transformer | [
"Ruoqi Yu",
"Shulei Wang"
] | 2008.03738 | 17,820 | https://openreview.net/forum?id=oOGqJ6Z1sA |
|
[] | Poster | [] | The majority of the research on the quantization of Deep Neural Networks (DNNs) is focused on reducing the precision of tensors visible by high-level frameworks (e.g., weights, activations, and gradients). However, current hardware still relies on high-accuracy core operations. Most significant is the operation of accumulating products. This high-precision accumulation operation is gradually becoming the main computational bottleneck. This is because, so far, the usage of low-precision accumulators led to a significant degradation in performance. In this work, we present a simple method to train and fine-tune DNNs, to allow, for the first time, utilization of cheaper, $12$-bits accumulators, with no significant degradation in accuracy. Lastly, we show that as we decrease the accumulation precision further, using fine-grained gradient approximations can improve the DNN accuracy. | [] | [] | Towards Cheaper Inference in Deep Networks with Lower Bit-Width Accumulators | [
"Yaniv Blumenfeld",
"Itay Hubara",
"Daniel Soudry"
] | 2401.14110 | 17,819 | https://openreview.net/forum?id=oOwDQl8haC |
|
[] | Poster | [] | We investigate the mechanism design problem faced by a principal who hires \emph{multiple} agents to gather and report costly information. Then, the principal exploits the information to make an informed decision. We model this problem as a game, where the principal announces a mechanism consisting in action recommendations and a payment function, a.k.a. scoring rule. Then, each agent chooses an effort level and receives partial information about an underlying state of nature based on the effort. Finally, the agents report the information (possibly non-truthfully), the principal takes a decision based on this information, and the agents are paid according to the scoring rule. While previous work focuses on single-agent problems, we consider multi-agents settings. This poses the challenge of coordinating the agents' efforts and aggregating correlated information. Indeed, we show that optimal mechanisms must correlate agents' efforts, which introduces externalities among the agents, and hence complex incentive compatibility constraints and equilibrium selection problems. First, we design a polynomial-time algorithm to find an optimal incentive compatible mechanism. Then, we study an online problem, where the principal repeatedly interacts with a group of unknown agents. We design a no-regret algorithm that provides $\widetilde{\mathcal{O}}(T^{2/3})$ regret with respect to an optimal mechanism, matching the state-of-the-art bound for single-agent settings. | [] | [] | Online Information Acquisition: Hiring Multiple Agents | [
"Federico Cacciamani",
"Matteo Castiglioni",
"Nicola Gatti"
] | 2307.06210 | 17,818 | https://openreview.net/forum?id=oQKKlzxV1o |
|
[] | Poster | [] | Standard language models generate text by selecting tokens from a fixed, finite, and standalone vocabulary. We introduce a novel method that selects context-aware phrases from a collection of supporting documents. One of the most significant challenges for this paradigm shift is determining the training oracles, because a string of text can be segmented in various ways and each segment can be retrieved from numerous possible documents. To address this, we propose to initialize the training oracles using linguistic heuristics and, more importantly, bootstrap the oracles through iterative self-reinforcement. Extensive experiments show that our model not only outperforms standard language models on a variety of knowledge-intensive tasks but also demonstrates improved generation quality in open-ended text generation. For instance, compared to the standard language model counterpart, our model raises the accuracy from 23.47% to 36.27% on OpenbookQA, and improves the MAUVE score from 42.61% to 81.58% in open-ended text generation. Remarkably, our model also achieves the best performance and the lowest latency among several retrieval-augmented baselines. In conclusion, we assert that retrieval is more accurate generation and hope that our work will encourage further research on this new paradigm shift. | [] | [] | Retrieval is Accurate Generation | [
"Bowen Cao",
"Deng Cai",
"Leyang Cui",
"Xuxin Cheng",
"Wei Bi",
"Yuexian Zou",
"Shuming Shi"
] | 2402.17532 | 17,816 | https://openreview.net/forum?id=oXYZJXDdo7 |
|
[] | Poster | [] | Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with learning to achieve multiple goals in an environment purely from offline datasets using sparse reward functions. Offline GCRL is pivotal for developing generalist agents capable of leveraging pre-existing datasets to learn diverse and reusable skills without hand-engineering reward functions. However, contemporary approaches to GCRL based on supervised learning and contrastive learning are often suboptimal in the offline setting. An alternative perspective on GCRL optimizes for occupancy matching, but necessitates learning a discriminator, which subsequently serves as a pseudo-reward for downstream RL. Inaccuracies in the learned discriminator can cascade, negatively influencing the resulting policy. We present a novel approach to GCRL under a new lens of mixture-distribution matching, leading to our discriminator-free method: SMORe. The key insight is combining the occupancy matching perspective of GCRL with a convex dual formulation to derive a learning objective that can better leverage suboptimal offline data. SMORe learns *scores* or unnormalized densities representing the importance of taking an action at a state for reaching a particular goal. SMORe is principled and our extensive experiments on the fully offline GCRL benchmark comprised of robot manipulation and locomotion tasks, including high-dimensional observations, show that SMORe can outperform state-of-the-art baselines by a significant margin. | [] | [] | Score Models for Offline Goal-Conditioned Reinforcement Learning | [
"Harshit Sikchi",
"Rohan Chitnis",
"Ahmed Touati",
"Alborz Geramifard",
"Amy Zhang",
"Scott Niekum"
] | 2311.02013 | 17,815 | https://openreview.net/forum?id=oXjnwQLcTA |
|
[] | Poster | [] | This paper presents a novel approach to premise selection, a crucial reasoning task in automated theorem proving. Traditionally, symbolic methods that rely on extensive domain knowledge and engineering effort are applied to this task. In contrast, this work demonstrates that contrastive training with the transformer architecture can achieve higher-quality retrieval of relevant premises, without the knowledge or feature engineering overhead. Our method, Magnushammer, outperforms the most advanced and widely used automation tool in interactive theorem proving called Sledgehammer. On the PISA and miniF2f benchmarks Magnushammer achieves $59.5\%$ (against $38.3\%$) and $34.0\%$ (against $20.9\%$) success rates, respectively. By combining Magnushammer with a language-model-based automated theorem prover, we further improve the state-of-the-art proof success rate from $57.0\%$ to $71.0\%$ on the PISA benchmark using $4$x fewer parameters. Moreover, we develop and open source a novel dataset for premise selection, containing textual representations of (proof state, relevant premise) pairs. To the best of our knowledge, this is the largest available premise selection dataset, and the first dataset of this kind for the Isabelle proof assistant. | [] | [] | Magnushammer: A Transformer-Based Approach to Premise Selection | [
"Maciej Mikuła",
"Szymon Tworkowski",
"Szymon Antoniak",
"Bartosz Piotrowski",
"Albert Q. Jiang",
"Jin Peng Zhou",
"Christian Szegedy",
"Łukasz Kuciński",
"Piotr Miłoś",
"Yuhuai Wu"
] | 2303.04488 | 17,814 | https://openreview.net/forum?id=oYjPk8mqAV |
|
[] | Poster | [
"https://github.com/YiyangZhou/LURE"
] | Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages. However, LVLMs still suffer from object hallucination, which is the problem of generating descriptions that include objects that do not actually exist in the images. This can negatively impact many vision-language tasks, such as visual summarization and reasoning. To address this issue, we propose a simple yet powerful algorithm, LVLM Hallucination Revisor (LURE), to post-hoc rectify object hallucination in LVLMs by reconstructing less hallucinatory descriptions. LURE is grounded in a rigorous statistical analysis of the key factors underlying object hallucination, including co-occurrence (the frequent appearance of certain objects alongside others in images), uncertainty (objects with higher uncertainty during LVLM decoding), and object position (hallucination often appears in the later part of the generated text). LURE can also be seamlessly integrated with any LVLMs. We evaluate LURE on six open-source LVLMs and found it outperforms the previous best approach in both general object hallucination evaluation metrics, GPT, and human evaluations. | [] | [] | Analyzing and Mitigating Object Hallucination in Large Vision-Language Models | [
"Yiyang Zhou",
"Chenhang Cui",
"Jaehong Yoon",
"Linjun Zhang",
"Zhun Deng",
"Chelsea Finn",
"Mohit Bansal",
"Huaxiu Yao"
] | 2310.00754 | 17,813 | https://openreview.net/forum?id=oZDJKTlOUe |
|
[] | Poster | [] | We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the private examples demonstrated in the prompt.We propose a novel algorithm that generates synthetic few-shot demonstrations from the private dataset with formal differential privacy (DP) guarantees, and show empirically that it can achieve effective ICL.We conduct extensive experiments on standard benchmarks and compare our algorithm with non-private ICL and zero-shot solutions. Our results demonstrate that our algorithm can achieve competitive performance with strong privacy levels.These results open up new possibilities for ICL with privacy protection for a broad range of applications. | [] | [] | Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation | [
"Xinyu Tang",
"Richard Shin",
"Huseyin A Inan",
"Andre Manoel",
"Fatemehsadat Mireshghallah",
"Zinan Lin",
"Sivakanth Gopi",
"Janardhan Kulkarni",
"Robert Sim"
] | 2309.11765 | 17,812 | https://openreview.net/forum?id=oZtt0pRnOl |
|
[] | Poster | [] | Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities.Dimensional collapse ––– also known as the "underfilling" phenomenon ––– is one of the major causes of degraded performance on downstream tasks. Previous work has investigated the dimensional collapse problem of SSL at a global level. In this paper, we demonstrate that representations can span over high dimensional space globally, but collapse locally. To address this, we propose a method called *local dimensionality regularization (LDReg)*. Our formulation is based on the derivation of the Fisher-Rao metric to compare and optimize local distance distributions at an asymptotically small radius for each data point. By increasing the local intrinsic dimensionality, we demonstrate through a range of experiments that LDReg improves the representation quality of SSL. The results also show that LDReg can regularize dimensionality at both local and global levels. | [] | [] | LDReg: Local Dimensionality Regularized Self-Supervised Learning | [
"Hanxun Huang",
"Ricardo J. G. B. Campello",
"Sarah Monazam Erfani",
"Xingjun Ma",
"Michael E. Houle",
"James Bailey"
] | 2401.10474 | 17,811 | https://openreview.net/forum?id=oZyAqjAjJW |
|
[] | Poster | [
"https://github.com/cosynus-lix/STAR"
] | Goal representation affects the performance of Hierarchical Reinforcement Learn- ing (HRL) algorithms by decomposing the complex learning problem into easier subtasks. Recent studies show that representations that preserve temporally ab- stract environment dynamics are successful in solving difficult problems and pro- vide theoretical guarantees for optimality. These methods however cannot scale to tasks where environment dynamics increase in complexity i.e. the temporally abstract transition relations depend on larger number of variables. On the other hand, other efforts have tried to use spatial abstraction to mitigate the previous issues. Their limitations include scalability to high dimensional environments and dependency on prior knowledge.In this paper, we propose a novel three-layer HRL algorithm that introduces, at different levels of the hierarchy, both a spatial and a temporal goal abstraction. We provide a theoretical study of the regret bounds of the learned policies. We evaluate the approach on complex continuous control tasks, demonstrating the effectiveness of spatial and temporal abstractions learned by this approach. | [] | [] | Reconciling Spatial and Temporal Abstractions for Goal Representation | [
"Mehdi Zadem",
"Sergio Mover",
"Sao Mai Nguyen"
] | 2401.09870 | 17,810 | https://openreview.net/forum?id=odY3PkI5VB |
|
[] | Poster | [] | The significant resource requirements associated with Large-scale Language Models (LLMs) have generated considerable interest in the development of techniques aimed at compressing and accelerating neural networks. Among these techniques, Post-Training Quantization (PTQ) has emerged as a subject of considerable interest due to its noteworthy compression efficiency and cost-effectiveness in the context of training.Existing PTQ methods for LLMs limit the optimization scope to scaling transformations between pre- and post-quantization weights. This constraint results in significant errors after quantization, particularly in low-bit configurations. In this paper, we advocate for the direct optimization using equivalent Affine transformations in PTQ (AffineQuant). This approach extends the optimization scope and thus significantly minimizing quantization errors. Additionally, by employing the corresponding inverse matrix, we can ensure equivalence between the pre- and post-quantization outputs of PTQ, thereby maintaining its efficiency and generalization capabilities. To ensure the invertibility of the transformation during optimization, we further introduce a gradual mask optimization method. This method initially focuses on optimizing the diagonal elements and gradually extends to the other elements. Such an approach aligns with the Levy-Desplanques theorem, theoretically ensuring invertibility of the transformation. As a result, significant performance improvements are evident across different LLMs on diverse datasets. Notably, these improvements are most pronounced when using very low-bit quantization, enabling the deployment of large models on edge devices. To illustrate, we attain a C4 perplexity of $14.89$ ({ 10.00$\downarrow$} vs $24.89$ in OmniQuant) on the LLaMA-$7$B model of W$2$A$16$ quantization. AffineQuant significantly outperforms OmniQuant on smaller models, achieving a perplexity of $42.29$ ({ 33.14$\downarrow$} vs $75.43$ in OmniQuant) when using $2$-bit $128$-group quantization for OPT-$125$M, which setting a new state-of-the-art benchmark for PTQ in LLMs. Codes are available in the supplementary materials. | [] | [] | AffineQuant: Affine Transformation Quantization for Large Language Models | [
"Yuexiao Ma",
"Huixia Li",
"Xiawu Zheng",
"Feng Ling",
"Xuefeng Xiao",
"Rui Wang",
"Shilei Wen",
"Fei Chao",
"Rongrong Ji"
] | 2403.12544 | 17,809 | https://openreview.net/forum?id=of2rhALq8l |
|
[] | Poster | [] | With the emergence of neural audio codecs, which encode multiple streams of discrete tokens from audio, large language models have recently gained attention as a promising approach for zero-shot Text-to-Speech (TTS) synthesis. Despite the ongoing rush towards scaling paradigms, audio tokenization ironically amplifies the scalability challenge, stemming from its long sequence length and the complexity of modelling the multiple sequences. To mitigate these issues, we present CLaM-TTS that employs a probabilistic residual vector quantization to 1) achieve superior compression in the token length, and 2) allow a language model to generate multiple tokens at once, thereby eliminating the need for cascaded modeling to handle the number of token streams. Our experimental results demonstrate that CLaM-TTS is better than or comparable to state-of-the-art zero-shot TTS baselines regarding naturalness, intelligibility, speaker similarity, and inference speed. In addition, we examine the impact of the pretraining extent of the language models and their text tokenization strategies on performances. | [] | [] | CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech | [
"Jaehyeon Kim",
"Keon Lee",
"Seungjun Chung",
"Jaewoong Cho"
] | 17,808 | https://openreview.net/forum?id=ofzeypWosV |
||
[] | Poster | [] | Accurate uncertainty measurement is a key step to building robust and reliable machine learning systems. Conformal prediction is a distribution-free uncertainty quantification algorithm popular for its ease of implementation, statistical coverage guarantees, and versatility for underlying forecasters. However, existing conformal prediction algorithms for time series are limited to single-step prediction without considering the temporal dependency. In this paper we propose the Copula Conformal Prediction algorithm for multivariate, multi-step Time Series forecasting, CopulaCPTS. We prove that CopulaCPTS has finite sample validity guarantee. On four synthetic and real-world multivariate time series datasets, we show that CopulaCPTS produces more calibrated and efficient confidence intervals for multi-step prediction tasks than existing techniques. | [] | [] | Copula Conformal prediction for multi-step time series prediction | [
"Sophia Huiwen Sun",
"Rose Yu"
] | 2212.03281 | 17,807 | https://openreview.net/forum?id=ojIJZDNIBj |
|
[] | Poster | [
"https://github.com/GitZH-Chen/LieBN.git"
] | Manifold-valued measurements exist in numerous applications within computer vision and machine learning. Recent studies have extended Deep Neural Networks (DNNs) to manifolds, and concomitantly, normalization techniques have also been adapted to several manifolds, referred to as Riemannian normalization. Nonetheless, most of the existing Riemannian normalization methods have been derived in an ad hoc manner and only apply to specific manifolds. This paper establishes a unified framework for Riemannian Batch Normalization (RBN) techniques on Lie groups. Our framework offers the theoretical guarantee of controlling both the Riemannian mean and variance. Empirically, we focus on Symmetric Positive Definite (SPD) manifolds, which possess three distinct types of Lie group structures. Using the deformation concept, we generalize the existing Lie groups on SPD manifolds into three families of parameterized Lie groups. Specific normalization layers induced by these Lie groups are then proposed for SPD neural networks. We demonstrate the effectiveness of our approach through three sets of experiments: radar recognition, human action recognition, and electroencephalography (EEG) classification. The code is available at https://github.com/GitZH-Chen/LieBN.git. | [] | [] | A Lie Group Approach to Riemannian Batch Normalization | [
"Ziheng Chen",
"Yue Song",
"Yunmei Liu",
"Nicu Sebe"
] | 2403.11261 | 17,806 | https://openreview.net/forum?id=okYdj8Ysru |
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