categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.18392 | null | null | http://arxiv.org/pdf/2405.18392v2 | 2024-05-29T16:56:26Z | 2024-05-28T17:33:54Z | Scaling Laws and Compute-Optimal Training Beyond Fixed Training
Durations | Scale has become a main ingredient in obtaining strong machine learning models. As a result, understanding a model's scaling properties is key to effectively designing both the right training setup as well as future generations of architectures. In this work, we argue that scale and training research has been needlessly complex due to reliance on the cosine schedule, which prevents training across different lengths for the same model size. We investigate the training behavior of a direct alternative - constant learning rate and cooldowns - and find that it scales predictably and reliably similar to cosine. Additionally, we show that stochastic weight averaging yields improved performance along the training trajectory, without additional training costs, across different scales. Importantly, with these findings we demonstrate that scaling experiments can be performed with significantly reduced compute and GPU hours by utilizing fewer but reusable training runs. Our code is available at https://github.com/epfml/schedules-and-scaling. | [
"['Alexander Hägele' 'Elie Bakouch' 'Atli Kosson' 'Loubna Ben Allal'\n 'Leandro Von Werra' 'Martin Jaggi']"
] |
null | null | 2405.18395 | null | null | http://arxiv.org/pdf/2405.18395v2 | 2024-06-03T03:53:16Z | 2024-05-28T17:35:05Z | MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit
Tests with Autocorrelations | A wide range of (multivariate) temporal (1D) and spatial (2D) data analysis tasks, such as grouping vehicle sensor trajectories, can be formulated as clustering with given metric constraints. Existing metric-constrained clustering algorithms overlook the rich correlation between feature similarity and metric distance, i.e., metric autocorrelation. The model-based variations of these clustering algorithms (e.g. TICC and STICC) achieve SOTA performance, yet suffer from computational instability and complexity by using a metric-constrained Expectation-Maximization procedure. In order to address these two problems, we propose a novel clustering algorithm, MC-GTA (Model-based Clustering via Goodness-of-fit Tests with Autocorrelations). Its objective is only composed of pairwise weighted sums of feature similarity terms (square Wasserstein-2 distance) and metric autocorrelation terms (a novel multivariate generalization of classic semivariogram). We show that MC-GTA is effectively minimizing the total hinge loss for intra-cluster observation pairs not passing goodness-of-fit tests, i.e., statistically not originating from the same distribution. Experiments on 1D/2D synthetic and real-world datasets demonstrate that MC-GTA successfully incorporates metric autocorrelation. It outperforms strong baselines by large margins (up to 14.3% in ARI and 32.1% in NMI) with faster and stabler optimization (>10x speedup). | [
"['Zhangyu Wang' 'Gengchen Mai' 'Krzysztof Janowicz' 'Ni Lao']"
] |
null | null | 2405.18400 | null | null | http://arxiv.org/pdf/2405.18400v3 | 2024-06-25T01:49:45Z | 2024-05-28T17:40:48Z | Superposed Decoding: Multiple Generations from a Single Autoregressive
Inference Pass | Many applications today provide users with multiple auto-complete drafts as they type, including GitHub's code completion, Gmail's smart compose, and Apple's messaging auto-suggestions. Under the hood, language models support this by running an autoregressive inference pass to provide a draft. Consequently, providing $k$ drafts to the user requires running an expensive language model $k$ times. To alleviate the computation cost of running $k$ inference passes, we propose Superposed Decoding, a new decoding algorithm that generates $k$ drafts at the computation cost of one autoregressive inference pass. We achieve this by feeding a superposition of the most recent token embeddings from the $k$ drafts as input to the next decoding step of the language model. At every inference step we combine the $k$ drafts with the top-$k$ tokens to get $k^2$ new drafts and cache the $k$ most likely options, using an n-gram interpolation with minimal compute overhead to filter out incoherent generations. Our experiments show that $k$ drafts from Superposed Decoding are at least as coherent and factual as Nucleus Sampling and Greedy Decoding respectively, while being at least $2.44times$ faster for $kge3$. In a compute-normalized setting, user evaluations demonstrably favor text generated by Superposed Decoding over Nucleus Sampling. Code and more examples open-sourced at https://github.com/RAIVNLab/SuperposedDecoding. | [
"['Ethan Shen' 'Alan Fan' 'Sarah M. Pratt' 'Jae Sung Park'\n 'Matthew Wallingford' 'Sham M. Kakade' 'Ari Holtzman' 'Ranjay Krishna'\n 'Ali Farhadi' 'Aditya Kusupati']"
] |
null | null | 2405.18401 | null | null | http://arxiv.org/pdf/2405.18401v1 | 2024-05-28T17:43:16Z | 2024-05-28T17:43:16Z | Explicit Formulae to Interchangeably use Hyperplanes and Hyperballs
using Inversive Geometry | Many algorithms require discriminative boundaries, such as separating hyperplanes or hyperballs, or are specifically designed to work on spherical data. By applying inversive geometry, we show that the two discriminative boundaries can be used interchangeably, and that general Euclidean data can be transformed into spherical data, whenever a change in point distances is acceptable. We provide explicit formulae to embed general Euclidean data into spherical data and to unembed it back. We further show a duality between hyperspherical caps, i.e., the volume created by a separating hyperplane on spherical data, and hyperballs and provide explicit formulae to map between the two. We further provide equations to translate inner products and Euclidean distances between the two spaces, to avoid explicit embedding and unembedding. We also provide a method to enforce projections of the general Euclidean space onto hemi-hyperspheres and propose an intrinsic dimensionality based method to obtain "all-purpose" parameters. To show the usefulness of the cap-ball-duality, we discuss example applications in machine learning and vector similarity search. | [
"['Erik Thordsen' 'Erich Schubert']"
] |
null | null | 2405.18407 | null | null | http://arxiv.org/pdf/2405.18407v1 | 2024-05-28T17:47:19Z | 2024-05-28T17:47:19Z | Phased Consistency Model | The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator. More details are available at https://g-u-n.github.io/projects/pcm/. | [
"['Fu-Yun Wang' 'Zhaoyang Huang' 'Alexander William Bergman' 'Dazhong Shen'\n 'Peng Gao' 'Michael Lingelbach' 'Keqiang Sun' 'Weikang Bian'\n 'Guanglu Song' 'Yu Liu' 'Hongsheng Li' 'Xiaogang Wang']"
] |
null | null | 2405.18414 | null | null | http://arxiv.org/pdf/2405.18414v1 | 2024-05-28T17:56:46Z | 2024-05-28T17:56:46Z | Don't Forget to Connect! Improving RAG with Graph-based Reranking | Retrieval Augmented Generation (RAG) has greatly improved the performance of Large Language Model (LLM) responses by grounding generation with context from existing documents. These systems work well when documents are clearly relevant to a question context. But what about when a document has partial information, or less obvious connections to the context? And how should we reason about connections between documents? In this work, we seek to answer these two core questions about RAG generation. We introduce G-RAG, a reranker based on graph neural networks (GNNs) between the retriever and reader in RAG. Our method combines both connections between documents and semantic information (via Abstract Meaning Representation graphs) to provide a context-informed ranker for RAG. G-RAG outperforms state-of-the-art approaches while having smaller computational footprint. Additionally, we assess the performance of PaLM 2 as a reranker and find it to significantly underperform G-RAG. This result emphasizes the importance of reranking for RAG even when using Large Language Models. | [
"['Jialin Dong' 'Bahare Fatemi' 'Bryan Perozzi' 'Lin F. Yang'\n 'Anton Tsitsulin']"
] |
null | null | 2405.18415 | null | null | http://arxiv.org/pdf/2405.18415v1 | 2024-05-28T17:57:06Z | 2024-05-28T17:57:06Z | Why are Visually-Grounded Language Models Bad at Image Classification? | Image classification is one of the most fundamental capabilities of machine vision intelligence. In this work, we revisit the image classification task using visually-grounded language models (VLMs) such as GPT-4V and LLaVA. We find that existing proprietary and public VLMs, despite often using CLIP as a vision encoder and having many more parameters, significantly underperform CLIP on standard image classification benchmarks like ImageNet. To understand the reason, we explore several hypotheses concerning the inference algorithms, training objectives, and data processing in VLMs. Our analysis reveals that the primary cause is data-related: critical information for image classification is encoded in the VLM's latent space but can only be effectively decoded with enough training data. Specifically, there is a strong correlation between the frequency of class exposure during VLM training and instruction-tuning and the VLM's performance in those classes; when trained with sufficient data, VLMs can match the accuracy of state-of-the-art classification models. Based on these findings, we enhance a VLM by integrating classification-focused datasets into its training, and demonstrate that the enhanced classification performance of the VLM transfers to its general capabilities, resulting in an improvement of 11.8% on the newly collected ImageWikiQA dataset. | [
"['Yuhui Zhang' 'Alyssa Unell' 'Xiaohan Wang' 'Dhruba Ghosh' 'Yuchang Su'\n 'Ludwig Schmidt' 'Serena Yeung-Levy']"
] |
null | null | 2405.18418 | null | null | http://arxiv.org/pdf/2405.18418v2 | 2024-05-31T17:03:00Z | 2024-05-28T17:57:23Z | Hierarchical World Models as Visual Whole-Body Humanoid Controllers | Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this difficulty. In this work, we explore highly data-driven approaches to visual whole-body humanoid control based on reinforcement learning, without any simplifying assumptions, reward design, or skill primitives. Specifically, we propose a hierarchical world model in which a high-level agent generates commands based on visual observations for a low-level agent to execute, both of which are trained with rewards. Our approach produces highly performant control policies in 8 tasks with a simulated 56-DoF humanoid, while synthesizing motions that are broadly preferred by humans. Code and videos: https://nicklashansen.com/rlpuppeteer | [
"['Nicklas Hansen' 'Jyothir S V' 'Vlad Sobal' 'Yann LeCun' 'Xiaolong Wang'\n 'Hao Su']"
] |
null | null | 2405.18427 | null | null | http://arxiv.org/pdf/2405.18427v1 | 2024-05-28T17:59:31Z | 2024-05-28T17:59:31Z | Classifying Overlapping Gaussian Mixtures in High Dimensions: From
Optimal Classifiers to Neural Nets | We derive closed-form expressions for the Bayes optimal decision boundaries in binary classification of high dimensional overlapping Gaussian mixture model (GMM) data, and show how they depend on the eigenstructure of the class covariances, for particularly interesting structured data. We empirically demonstrate, through experiments on synthetic GMMs inspired by real-world data, that deep neural networks trained for classification, learn predictors which approximate the derived optimal classifiers. We further extend our study to networks trained on authentic data, observing that decision thresholds correlate with the covariance eigenvectors rather than the eigenvalues, mirroring our GMM analysis. This provides theoretical insights regarding neural networks' ability to perform probabilistic inference and distill statistical patterns from intricate distributions. | [
"['Khen Cohen' 'Noam Levi' 'Yaron Oz']"
] |
null | null | 2405.18432 | null | null | http://arxiv.org/pdf/2405.18432v1 | 2024-05-28T17:59:51Z | 2024-05-28T17:59:51Z | On the Origin of Llamas: Model Tree Heritage Recovery | The rapid growth of neural network models shared on the internet has made model weights an important data modality. However, this information is underutilized as the weights are uninterpretable, and publicly available models are disorganized. Inspired by Darwin's tree of life, we define the Model Tree which describes the origin of models i.e., the parent model that was used to fine-tune the target model. Similarly to the natural world, the tree structure is unknown. In this paper, we introduce the task of Model Tree Heritage Recovery (MoTHer Recovery) for discovering Model Trees in the ever-growing universe of neural networks. Our hypothesis is that model weights encode this information, the challenge is to decode the underlying tree structure given the weights. Beyond the immediate application of model authorship attribution, MoTHer recovery holds exciting long-term applications akin to indexing the internet by search engines. Practically, for each pair of models, this task requires: i) determining if they are related, and ii) establishing the direction of the relationship. We find that certain distributional properties of the weights evolve monotonically during training, which enables us to classify the relationship between two given models. MoTHer recovery reconstructs entire model hierarchies, represented by a directed tree, where a parent model gives rise to multiple child models through additional training. Our approach successfully reconstructs complex Model Trees, as well as the structure of "in-the-wild" model families such as Llama 2 and Stable Diffusion. | [
"['Eliahu Horwitz' 'Asaf Shul' 'Yedid Hoshen']"
] |
null | null | 2405.18444 | null | null | http://arxiv.org/pdf/2405.18444v1 | 2024-05-24T14:10:22Z | 2024-05-24T14:10:22Z | Discovering deposition process regimes: leveraging unsupervised learning
for process insights, surrogate modeling, and sensitivity analysis | This work introduces a comprehensive approach utilizing data-driven methods to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors and the interplay of physical mechanism that dominate in each one of them. Through this work, we address three key objectives. Firstly, our methodology relies on process outcomes, derived by a detailed CFD model, to identify clusters of "outcomes" corresponding to distinct process regimes, wherein the relative influence of input variables undergoes notable shifts. This phenomenon is experimentally validated through Arrhenius plot analysis, affirming the efficacy of our approach. Secondly, we demonstrate the development of an efficient surrogate model, based on Polynomial Chaos Expansion (PCE), that maintains accuracy, facilitating streamlined computational analyses. Finally, as a result of PCE, sensitivity analysis is made possible by means of Sobol' indices, that quantify the impact of process inputs across identified regimes. The insights gained from our analysis contribute to the formulation of hypotheses regarding phenomena occurring beyond the transition regime. Notably, the significance of temperature even in the diffusion-limited regime, as evidenced by the Arrhenius plot, suggests activation of gas phase reactions at elevated temperatures. Importantly, our proposed methods yield insights that align with experimental observations and theoretical principles, aiding decision-making in process design and optimization. By circumventing the need for costly and time-consuming experiments, our approach offers a pragmatic pathway towards enhanced process efficiency. Moreover, this study underscores the potential of data-driven computational methods for innovating reactor design paradigms. | [
"['Geremy Loachamín Suntaxi' 'Paris Papavasileiou' 'Eleni D. Koronaki'\n 'Dimitrios G. Giovanis' 'Georgios Gakis' 'Ioannis G. Aviziotis'\n 'Martin Kathrein' 'Gabriele Pozzetti' 'Christoph Czettl'\n 'Stéphane P. A. Bordas' 'Andreas G. Boudouvis']"
] |
null | null | 2405.18449 | null | null | http://arxiv.org/pdf/2405.18449v1 | 2024-05-28T03:06:10Z | 2024-05-28T03:06:10Z | Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for
Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and
Siamese Networks | WHO has declared that more than 2.2 billion people worldwide are suffering from visual disorders, such as media haze, glaucoma, and drusen. At least 1 billion of these cases could have been either prevented or successfully treated, yet they remain unaddressed due to poverty, a lack of specialists, inaccurate ocular fundus diagnoses by ophthalmologists, or the presence of a rare disease. To address this, the research has developed the Hybrid Trio-Network Model Algorithm for accurately diagnosing 12 distinct common and rare eye diseases. This algorithm utilized the RFMiD dataset of 3,200 fundus images and the Binary Relevance Method to detect diseases separately, ensuring expandability and avoiding incorrect correlations. Each detector, incorporating finely tuned hyperparameters to optimize performance, consisted of three feature components: A classical transfer learning CNN model, a two-stage CNN model, and a Siamese Network. The diagnosis was made using features extracted through this Trio-Model with Ensembled Machine Learning algorithms. The proposed model achieved an average accuracy of 97% and an AUC score of 0.96. Compared to past benchmark studies, an increase of over 10% in the F1-score was observed for most diseases. Furthermore, using the Siamese Network, the model successfully made predictions in diseases like optic disc pallor, which past studies failed to predict due to low confidence. This diagnostic tool presents a stable, adaptive, cost-effective, efficient, accessible, and fast solution for globalizing early detection of both common and rare diseases. | [
"['Yavuz Selim Inan']"
] |
null | null | 2405.18457 | null | null | http://arxiv.org/pdf/2405.18457v2 | 2024-06-06T10:54:31Z | 2024-05-28T16:58:37Z | Improving Linear System Solvers for Hyperparameter Optimisation in
Iterative Gaussian Processes | Scaling hyperparameter optimisation to very large datasets remains an open problem in the Gaussian process community. This paper focuses on iterative methods, which use linear system solvers, like conjugate gradients, alternating projections or stochastic gradient descent, to construct an estimate of the marginal likelihood gradient. We discuss three key improvements which are applicable across solvers: (i) a pathwise gradient estimator, which reduces the required number of solver iterations and amortises the computational cost of making predictions, (ii) warm starting linear system solvers with the solution from the previous step, which leads to faster solver convergence at the cost of negligible bias, (iii) early stopping linear system solvers after a limited computational budget, which synergises with warm starting, allowing solver progress to accumulate over multiple marginal likelihood steps. These techniques provide speed-ups of up to $72times$ when solving to tolerance, and decrease the average residual norm by up to $7times$ when stopping early. | [
"['Jihao Andreas Lin' 'Shreyas Padhy' 'Bruno Mlodozeniec' 'Javier Antorán'\n 'José Miguel Hernández-Lobato']"
] |
null | null | 2405.18458 | null | null | http://arxiv.org/pdf/2405.18458v1 | 2024-05-28T17:27:20Z | 2024-05-28T17:27:20Z | Asymmetrical estimator for training grey-box deep photonic neural
networks | Physical neural networks (PNNs) are emerging paradigms for neural network acceleration due to their high-bandwidth, in-propagation analogue processing. Despite the advantages of PNN for inference, training remains a challenge. The imperfect information of the physical transformation means the failure of conventional gradient-based updates from backpropagation (BP). Here, we present the asymmetrical training (AT) method, which treats the PNN structure as a grey box. AT performs training while only knowing the last layer output and neuron topological connectivity of a deep neural network structure, not requiring information about the physical control-transformation mapping. We experimentally demonstrated the AT method on deep grey-box PNNs implemented by uncalibrated photonic integrated circuits (PICs), improving the classification accuracy of Iris flower and modified MNIST hand-written digits from random guessing to near theoretical maximum. We also showcased the consistently enhanced performance of AT over BP for different datasets, including MNIST, fashion-MNIST, and Kuzushiji-MNIST. The AT method demonstrated successful training with minimal hardware overhead and reduced computational overhead, serving as a robust light-weight training alternative to fully explore the advantages of physical computation. | [
"['Yizhi Wang' 'Minjia Chen' 'Chunhui Yao' 'Jie Ma' 'Ting Yan'\n 'Richard Penty' 'Qixiang Cheng']"
] |
null | null | 2405.18459 | null | null | http://arxiv.org/pdf/2405.18459v1 | 2024-05-28T17:44:35Z | 2024-05-28T17:44:35Z | Probing the Information Theoretical Roots of Spatial Dependence Measures | Intuitively, there is a relation between measures of spatial dependence and information theoretical measures of entropy. For instance, we can provide an intuition of why spatial data is special by stating that, on average, spatial data samples contain less than expected information. Similarly, spatial data, e.g., remotely sensed imagery, that is easy to compress is also likely to show significant spatial autocorrelation. Formulating our (highly specific) core concepts of spatial information theory in the widely used language of information theory opens new perspectives on their differences and similarities and also fosters cross-disciplinary collaboration, e.g., with the broader AI/ML communities. Interestingly, however, this intuitive relation is challenging to formalize and generalize, leading prior work to rely mostly on experimental results, e.g., for describing landscape patterns. In this work, we will explore the information theoretical roots of spatial autocorrelation, more specifically Moran's I, through the lens of self-information (also known as surprisal) and provide both formal proofs and experiments. | [
"['Zhangyu Wang' 'Krzysztof Janowicz' 'Gengchen Mai' 'Ivan Majic']"
] |
null | null | 2405.18461 | null | null | http://arxiv.org/pdf/2405.18461v1 | 2024-05-28T17:49:24Z | 2024-05-28T17:49:24Z | Why Algorithms Remain Unjust: Power Structures Surrounding Algorithmic
Activity | Algorithms play an increasingly-significant role in our social lives. Unfortunately, they often perpetuate social injustices while doing so. The popular means of addressing these algorithmic injustices has been through algorithmic reformism: fine-tuning the algorithm itself to be more fair, accountable, and transparent. While commendable, the emerging discipline of critical algorithm studies shows that reformist approaches have failed to curtail algorithmic injustice because they ignore the power structure surrounding algorithms. Heeding calls from critical algorithm studies to analyze this power structure, I employ a framework developed by Erik Olin Wright to examine the configuration of power surrounding Algorithmic Activity: the ways in which algorithms are researched, developed, trained, and deployed within society. I argue that the reason Algorithmic Activity is unequal, undemocratic, and unsustainable is that the power structure shaping it is one of economic empowerment rather than social empowerment. For Algorithmic Activity to be socially just, we need to transform this power configuration to empower the people at the other end of an algorithm. To this end, I explore Wright's symbiotic, interstitial, and raptural transformations in the context of Algorithmic Activity, as well as how they may be applied in a hypothetical research project that uses algorithms to address a social issue. I conclude with my vision for socially just Algorithmic Activity, asking that future work strives to integrate the proposed transformations and develop new mechanisms for social empowerment. | [
"['Andrew Balch']"
] |
null | null | 2405.18471 | null | null | http://arxiv.org/pdf/2405.18471v1 | 2024-05-28T18:00:01Z | 2024-05-28T18:00:01Z | Symbolic Regression for Beyond the Standard Model Physics | We propose symbolic regression as a powerful tool for studying Beyond the Standard Model physics. As a benchmark model, we consider the so-called Constrained Minimal Supersymmetric Standard Model, which has a four-dimensional parameter space defined at the GUT scale. We provide a set of analytical expressions that reproduce three low-energy observables of interest in terms of the parameters of the theory: the Higgs mass, the contribution to the anomalous magnetic moment of the muon, and the cold dark matter relic density. To demonstrate the power of the approach, we employ the symbolic expressions in a global fits analysis to derive the posterior probability densities of the parameters, which are obtained extremely rapidly in comparison with conventional methods. | [
"['Shehu AbdusSalam' 'Steve Abel' 'Miguel Crispim Romao']"
] |
null | null | 2405.18489 | null | null | http://arxiv.org/pdf/2405.18489v1 | 2024-05-28T18:00:32Z | 2024-05-28T18:00:32Z | Predicting Ground State Properties: Constant Sample Complexity and Deep
Learning Algorithms | A fundamental problem in quantum many-body physics is that of finding ground states of local Hamiltonians. A number of recent works gave provably efficient machine learning (ML) algorithms for learning ground states. Specifically, [Huang et al. Science 2022], introduced an approach for learning properties of the ground state of an $n$-qubit gapped local Hamiltonian $H$ from only $n^{mathcal{O}(1)}$ data points sampled from Hamiltonians in the same phase of matter. This was subsequently improved by [Lewis et al. Nature Communications 2024], to $mathcal{O}(log n)$ samples when the geometry of the $n$-qubit system is known. In this work, we introduce two approaches that achieve a constant sample complexity, independent of system size $n$, for learning ground state properties. Our first algorithm consists of a simple modification of the ML model used by Lewis et al. and applies to a property of interest known beforehand. Our second algorithm, which applies even if a description of the property is not known, is a deep neural network model. While empirical results showing the performance of neural networks have been demonstrated, to our knowledge, this is the first rigorous sample complexity bound on a neural network model for predicting ground state properties. We also perform numerical experiments that confirm the improved scaling of our approach compared to earlier results. | [
"['Marc Wanner' 'Laura Lewis' 'Chiranjib Bhattacharyya' 'Devdatt Dubhashi'\n 'Alexandru Gheorghiu']"
] |
null | null | 2405.18498 | null | null | http://arxiv.org/pdf/2405.18498v1 | 2024-05-28T18:09:22Z | 2024-05-28T18:09:22Z | The Unified Balance Theory of Second-Moment Exponential Scaling
Optimizers in Visual Tasks | We have identified a potential method for unifying first-order optimizers through the use of variable Second-Moment Exponential Scaling(SMES). We begin with back propagation, addressing classic phenomena such as gradient vanishing and explosion, as well as issues related to dataset sparsity, and introduce the theory of balance in optimization. Through this theory, we suggest that SGD and adaptive optimizers can be unified under a broader inference, employing variable moving exponential scaling to achieve a balanced approach within a generalized formula for first-order optimizers. We conducted tests on some classic datasets and networks to confirm the impact of different balance coefficients on the overall training process. | [
"['Gongyue Zhang' 'Honghai Liu']"
] |
null | null | 2405.18499 | null | null | http://arxiv.org/pdf/2405.18499v1 | 2024-05-28T18:10:45Z | 2024-05-28T18:10:45Z | Large Margin Discriminative Loss for Classification | In this paper, we introduce a novel discriminative loss function with large margin in the context of Deep Learning. This loss boosts the discriminative power of neural nets, represented by intra-class compactness and inter-class separability. On the one hand, the class compactness is ensured by close distance of samples of the same class to each other. On the other hand, the inter-class separability is boosted by a margin loss that ensures the minimum distance of each class to its closest boundary. All the terms in our loss have an explicit meaning, giving a direct view of the feature space obtained. We analyze mathematically the relation between compactness and margin term, giving a guideline about the impact of the hyper-parameters on the learned features. Moreover, we also analyze properties of the gradient of the loss with respect to the parameters of the neural net. Based on this, we design a strategy called partial momentum updating that enjoys simultaneously stability and consistency in training. Furthermore, we also investigate generalization errors to have better theoretical insights. Our loss function systematically boosts the test accuracy of models compared to the standard softmax loss in our experiments. | [
"['Hai-Vy Nguyen' 'Fabrice Gamboa' 'Sixin Zhang' 'Reda Chhaibi'\n 'Serge Gratton' 'Thierry Giaccone']"
] |
null | null | 2405.18503 | null | null | http://arxiv.org/pdf/2405.18503v2 | 2024-06-10T20:49:58Z | 2024-05-28T18:14:52Z | SoundCTM: Uniting Score-based and Consistency Models for Text-to-Sound
Generation | Sound content is an indispensable element for multimedia works such as video games, music, and films. Recent high-quality diffusion-based sound generation models can serve as valuable tools for the creators. However, despite producing high-quality sounds, these models often suffer from slow inference speeds. This drawback burdens creators, who typically refine their sounds through trial and error to align them with their artistic intentions. To address this issue, we introduce Sound Consistency Trajectory Models (SoundCTM). Our model enables flexible transitioning between high-quality 1-step sound generation and superior sound quality through multi-step generation. This allows creators to initially control sounds with 1-step samples before refining them through multi-step generation. While CTM fundamentally achieves flexible 1-step and multi-step generation, its impressive performance heavily depends on an additional pretrained feature extractor and an adversarial loss, which are expensive to train and not always available in other domains. Thus, we reframe CTM's training framework and introduce a novel feature distance by utilizing the teacher's network for a distillation loss. Additionally, while distilling classifier-free guided trajectories, we train conditional and unconditional student models simultaneously and interpolate between these models during inference. We also propose training-free controllable frameworks for SoundCTM, leveraging its flexible sampling capability. SoundCTM achieves both promising 1-step and multi-step real-time sound generation without using any extra off-the-shelf networks. Furthermore, we demonstrate SoundCTM's capability of controllable sound generation in a training-free manner. Our codes, pretrained models, and audio samples are available at https://github.com/sony/soundctm. | [
"['Koichi Saito' 'Dongjun Kim' 'Takashi Shibuya' 'Chieh-Hsin Lai'\n 'Zhi Zhong' 'Yuhta Takida' 'Yuki Mitsufuji']"
] |
null | null | 2405.18507 | null | null | http://arxiv.org/pdf/2405.18507v2 | 2024-07-13T12:06:07Z | 2024-05-28T18:24:16Z | Injecting Hierarchical Biological Priors into Graph Neural Networks for
Flow Cytometry Prediction | In the complex landscape of hematologic samples such as peripheral blood or bone marrow derived from flow cytometry (FC) data, cell-level prediction presents profound challenges. This work explores injecting hierarchical prior knowledge into graph neural networks (GNNs) for single-cell multi-class classification of tabular cellular data. By representing the data as graphs and encoding hierarchical relationships between classes, we propose our hierarchical plug-in method to be applied to several GNN models, namely, FCHC-GNN, and effectively designed to capture neighborhood information crucial for single-cell FC domain. Extensive experiments on our cohort of 19 distinct patients, demonstrate that incorporating hierarchical biological constraints boosts performance significantly across multiple metrics compared to baseline GNNs without such priors. The proposed approach highlights the importance of structured inductive biases for gaining improved generalization in complex biological prediction tasks. | [
"['Fatemeh Nassajian Mojarrad' 'Lorenzo Bini' 'Thomas Matthes'\n 'Stéphane Marchand-Maillet']"
] |
null | null | 2405.18512 | null | null | http://arxiv.org/pdf/2405.18512v1 | 2024-05-28T18:31:14Z | 2024-05-28T18:31:14Z | Understanding Transformer Reasoning Capabilities via Graph Algorithms | Which transformer scaling regimes are able to perfectly solve different classes of algorithmic problems? While tremendous empirical advances have been attained by transformer-based neural networks, a theoretical understanding of their algorithmic reasoning capabilities in realistic parameter regimes is lacking. We investigate this question in terms of the network's depth, width, and number of extra tokens for algorithm execution. Our novel representational hierarchy separates 9 algorithmic reasoning problems into classes solvable by transformers in different realistic parameter scaling regimes. We prove that logarithmic depth is necessary and sufficient for tasks like graph connectivity, while single-layer transformers with small embedding dimensions can solve contextual retrieval tasks. We also support our theoretical analysis with ample empirical evidence using the GraphQA benchmark. These results show that transformers excel at many graph reasoning tasks, even outperforming specialized graph neural networks. | [
"['Clayton Sanford' 'Bahare Fatemi' 'Ethan Hall' 'Anton Tsitsulin'\n 'Mehran Kazemi' 'Jonathan Halcrow' 'Bryan Perozzi' 'Vahab Mirrokni']"
] |
null | null | 2405.18515 | null | null | http://arxiv.org/pdf/2405.18515v1 | 2024-05-28T18:33:18Z | 2024-05-28T18:33:18Z | Atlas3D: Physically Constrained Self-Supporting Text-to-3D for
Simulation and Fabrication | Existing diffusion-based text-to-3D generation methods primarily focus on producing visually realistic shapes and appearances, often neglecting the physical constraints necessary for downstream tasks. Generated models frequently fail to maintain balance when placed in physics-based simulations or 3D printed. This balance is crucial for satisfying user design intentions in interactive gaming, embodied AI, and robotics, where stable models are needed for reliable interaction. Additionally, stable models ensure that 3D-printed objects, such as figurines for home decoration, can stand on their own without requiring additional supports. To fill this gap, we introduce Atlas3D, an automatic and easy-to-implement method that enhances existing Score Distillation Sampling (SDS)-based text-to-3D tools. Atlas3D ensures the generation of self-supporting 3D models that adhere to physical laws of stability under gravity, contact, and friction. Our approach combines a novel differentiable simulation-based loss function with physically inspired regularization, serving as either a refinement or a post-processing module for existing frameworks. We verify Atlas3D's efficacy through extensive generation tasks and validate the resulting 3D models in both simulated and real-world environments. | [
"['Yunuo Chen' 'Tianyi Xie' 'Zeshun Zong' 'Xuan Li' 'Feng Gao' 'Yin Yang'\n 'Ying Nian Wu' 'Chenfanfu Jiang']"
] |
null | null | 2405.18518 | null | null | http://arxiv.org/pdf/2405.18518v1 | 2024-05-28T18:38:15Z | 2024-05-28T18:38:15Z | LSTM-COX Model: A Concise and Efficient Deep Learning Approach for
Handling Recurrent Events | In the current field of clinical medicine, traditional methods for analyzing recurrent events have limitations when dealing with complex time-dependent data. This study combines Long Short-Term Memory networks (LSTM) with the Cox model to enhance the model's performance in analyzing recurrent events with dynamic temporal information. Compared to classical models, the LSTM-Cox model significantly improves the accuracy of extracting clinical risk features and exhibits lower Akaike Information Criterion (AIC) values, while maintaining good performance on simulated datasets. In an empirical analysis of bladder cancer recurrence data, the model successfully reduced the mean squared error during the training phase and achieved a Concordance index of up to 0.90 on the test set. Furthermore, the model effectively distinguished between high and low-risk patient groups, and the identified recurrence risk features such as the number of tumor recurrences and maximum size were consistent with other research and clinical trial results. This study not only provides a straightforward and efficient method for analyzing recurrent data and extracting features but also offers a convenient pathway for integrating deep learning techniques into clinical risk prediction systems. | [
"['Zhang Runquan' 'Shi Xiaoping']"
] |
null | null | 2405.18520 | null | null | http://arxiv.org/pdf/2405.18520v1 | 2024-05-28T18:38:46Z | 2024-05-28T18:38:46Z | Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical
Behaviors in Deep Off-Policy RL | Off-policy reinforcement learning (RL) has achieved notable success in tackling many complex real-world tasks, by leveraging previously collected data for policy learning. However, most existing off-policy RL algorithms fail to maximally exploit the information in the replay buffer, limiting sample efficiency and policy performance. In this work, we discover that concurrently training an offline RL policy based on the shared online replay buffer can sometimes outperform the original online learning policy, though the occurrence of such performance gains remains uncertain. This motivates a new possibility of harnessing the emergent outperforming offline optimal policy to improve online policy learning. Based on this insight, we present Offline-Boosted Actor-Critic (OBAC), a model-free online RL framework that elegantly identifies the outperforming offline policy through value comparison, and uses it as an adaptive constraint to guarantee stronger policy learning performance. Our experiments demonstrate that OBAC outperforms other popular model-free RL baselines and rivals advanced model-based RL methods in terms of sample efficiency and asymptotic performance across 53 tasks spanning 6 task suites. | [
"['Yu Luo' 'Tianying Ji' 'Fuchun Sun' 'Jianwei Zhang' 'Huazhe Xu'\n 'Xianyuan Zhan']"
] |
null | null | 2405.18536 | null | null | http://arxiv.org/pdf/2405.18536v1 | 2024-05-28T19:07:12Z | 2024-05-28T19:07:12Z | Data-Driven Simulator for Mechanical Circulatory Support with Domain
Adversarial Neural Process | Mechanical Circulatory Support (MCS) devices, implemented as a probabilistic deep sequence model. Existing mechanical simulators for MCS rely on oversimplifying assumptions and are insensitive to patient-specific behavior, limiting their applicability to real-world treatment scenarios. To address these shortcomings, our model Domain Adversarial Neural Process (DANP) employs a neural process architecture, allowing it to capture the probabilistic relationship between MCS pump levels and aortic pressure measurements with uncertainty. We use domain adversarial training to combine simulation data with real-world observations, resulting in a more realistic and diverse representation of potential outcomes. Empirical results with an improvement of 19% in non-stationary trend prediction establish DANP as an effective tool for clinicians to understand and make informed decisions regarding MCS patient treatment. | [
"['Sophia Sun' 'Wenyuan Chen' 'Zihao Zhou' 'Sonia Fereidooni'\n 'Elise Jortberg' 'Rose Yu']"
] |
null | null | 2405.18537 | null | null | http://arxiv.org/pdf/2405.18537v1 | 2024-05-28T19:10:47Z | 2024-05-28T19:10:47Z | Augmented Conversation with Embedded Speech-Driven On-the-Fly
Referencing in AR | This paper introduces the concept of augmented conversation, which aims to support co-located in-person conversations via embedded speech-driven on-the-fly referencing in augmented reality (AR). Today computing technologies like smartphones allow quick access to a variety of references during the conversation. However, these tools often create distractions, reducing eye contact and forcing users to focus their attention on phone screens and manually enter keywords to access relevant information. In contrast, AR-based on-the-fly referencing provides relevant visual references in real-time, based on keywords extracted automatically from the spoken conversation. By embedding these visual references in AR around the conversation partner, augmented conversation reduces distraction and friction, allowing users to maintain eye contact and supporting more natural social interactions. To demonstrate this concept, we developed system, a Hololens-based interface that leverages real-time speech recognition, natural language processing and gaze-based interactions for on-the-fly embedded visual referencing. In this paper, we explore the design space of visual referencing for conversations, and describe our our implementation -- building on seven design guidelines identified through a user-centered design process. An initial user study confirms that our system decreases distraction and friction in conversations compared to smartphone searches, while providing highly useful and relevant information. | [
"['Shivesh Jadon' 'Mehrad Faridan' 'Edward Mah' 'Rajan Vaish'\n 'Wesley Willett' 'Ryo Suzuki']"
] |
null | null | 2405.18540 | null | null | http://arxiv.org/pdf/2405.18540v1 | 2024-05-28T19:16:17Z | 2024-05-28T19:16:17Z | Learning diverse attacks on large language models for robust red-teaming
and safety tuning | Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches. | [
"['Seanie Lee' 'Minsu Kim' 'Lynn Cherif' 'David Dobre' 'Juho Lee'\n 'Sung Ju Hwang' 'Kenji Kawaguchi' 'Gauthier Gidel' 'Yoshua Bengio'\n 'Nikolay Malkin' 'Moksh Jain']"
] |
null | null | 2405.18542 | null | null | http://arxiv.org/abs/2405.18542v1 | 2024-05-28T19:17:48Z | 2024-05-28T19:17:48Z | Automatic detection of cognitive impairment in elderly people using an
entertainment chatbot with Natural Language Processing capabilities | Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. Automatic chatbot dialogue stages allow assessing content description skills and detecting cognitive impairment with Machine Learning algorithms. We create these dialogue flows automatically from updated news items using Natural Language Generation techniques. The system also infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically by comparing these answers with the user responses. It employs a similarity metric with values in [0, 1], in increasing level of similarity. To evaluate the performance and usability of our approach, we have conducted field tests with a test group of 30 elderly people in the earliest stages of dementia, under the supervision of gerontologists. In the experiments, we have analysed the effect of stress and concentration in these users. Those without cognitive impairment performed up to five times better. In particular, the similarity metric varied between 0.03, for stressed and unfocused participants, and 0.36, for relaxed and focused users. Finally, we developed a Machine Learning algorithm based on textual analysis features for automatic cognitive impairment detection, which attained accuracy, F-measure and recall levels above 80%. We have thus validated the automatic approach to detect cognitive impairment in elderly people based on entertainment content. | [
"['Francisco de Arriba-Pérez' 'Silvia García-Méndez'\n 'Francisco J. González-Castaño' 'Enrique Costa-Montenegro']"
] |
null | null | 2405.18548 | null | null | http://arxiv.org/pdf/2405.18548v1 | 2024-05-28T19:30:43Z | 2024-05-28T19:30:43Z | The Computational Complexity of Formal Reasoning for Encoder-Only
Transformers | We investigate challenges and possibilities of formal reasoning for encoder-only transformers (EOT), meaning sound and complete methods for verifying or interpreting behaviour. In detail, we condense related formal reasoning tasks in the form of a naturally occurring satisfiability problem (SAT). We find that SAT is undecidable if we consider EOT, commonly considered in the expressiveness community. Furthermore, we identify practical scenarios where SAT is decidable and establish corresponding complexity bounds. Besides trivial cases, we find that quantized EOT, namely those restricted by some fixed-width arithmetic, lead to the decidability of SAT due to their limited attention capabilities. However, the problem remains difficult, as we establish those scenarios where SAT is NEXPTIME-hard and those where we can show that it is solvable in NEXPTIME for quantized EOT. To complement our theoretical results, we put our findings and their implications in the overall perspective of formal reasoning. | [
"['Marco Sälzer' 'Eric Alsmann' 'Martin Lange']"
] |
null | null | 2405.18549 | null | null | http://arxiv.org/pdf/2405.18549v1 | 2024-05-28T19:36:55Z | 2024-05-28T19:36:55Z | Learning from Uncertain Data: From Possible Worlds to Possible Models | We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive multiplicity. Our approach leverages abstract interpretation and zonotopes, a type of convex polytope, to compactly represent these dataset variations, enabling the symbolic execution of gradient descent on all possible worlds simultaneously. We develop techniques to ensure that this process converges to a fixed point and derive closed-form solutions for this fixed point. Our method provides sound over-approximations of all possible optimal models and viable prediction ranges. We demonstrate the effectiveness of our approach through theoretical and empirical analysis, highlighting its potential to reason about model and prediction uncertainty due to data quality issues in training data. | [
"['Jiongli Zhu' 'Su Feng' 'Boris Glavic' 'Babak Salimi']"
] |
null | null | 2405.18552 | null | null | http://arxiv.org/pdf/2405.18552v1 | 2024-05-28T19:54:26Z | 2024-05-28T19:54:26Z | SGD method for entropy error function with smoothing l0 regularization
for neural networks | The entropy error function has been widely used in neural networks. Nevertheless, the network training based on this error function generally leads to a slow convergence rate, and can easily be trapped in a local minimum or even with the incorrect saturation problem in practice. In fact, there are many results based on entropy error function in neural network and its applications. However, the theory of such an algorithm and its convergence have not been fully studied so far. To tackle the issue, we propose a novel entropy function with smoothing l0 regularization for feed-forward neural networks. Using real-world datasets, we performed an empirical evaluation to demonstrate that the newly conceived algorithm allows us to substantially improve the prediction performance of the considered neural networks. More importantly, the experimental results also show that our proposed function brings in more precise classifications, compared to well-founded baselines. Our work is novel as it enables neural networks to learn effectively, producing more accurate predictions compared to state-of-the-art algorithms. In this respect, we expect that the algorithm will contribute to existing studies in the field, advancing research in Machine Learning and Deep Learning. | [
"['Trong-Tuan Nguyen' 'Van-Dat Thang' 'Nguyen Van Thin' 'Phuong T. Nguyen']"
] |
null | null | 2405.18554 | null | null | http://arxiv.org/pdf/2405.18554v1 | 2024-05-28T19:56:53Z | 2024-05-28T19:56:53Z | Scalable Surrogate Verification of Image-based Neural Network Control
Systems using Composition and Unrolling | Verifying safety of neural network control systems that use images as input is a difficult problem because, from a given system state, there is no known way to mathematically model what images are possible in the real-world. We build on recent work that considers a surrogate verification approach, training a conditional generative adversarial network (cGAN) as an image generator in place of the real world. This enables set-based formal analysis of the closed-loop system, providing analysis beyond simulation and testing. While existing work is effective on small examples, excessive overapproximation both within a single control period and across multiple control periods limits its scalability. We propose approaches to overcome these two sources of error. First, we overcome one-step error by composing the system's dynamics along with the cGAN and neural network controller, without losing the dependencies between input states and the control outputs as in the monotonic analysis of the system dynamics. Second, we reduce multi-step error by repeating the single-step composition, essentially unrolling multiple steps of the control loop into a large neural network. We then leverage existing network verification tools to compute accurate reachable sets for multiple steps, avoiding the accumulation of abstraction error at each step. We demonstrate the effectiveness of our approach in terms of both accuracy and scalability using two case studies: an autonomous aircraft taxiing system and an advanced emergency braking system. On the aircraft taxiing system, the converged reachable set is 175% larger using the prior baseline method compared with our proposed approach. On the emergency braking system, with 24x the number of image output variables from the cGAN, the baseline method fails to prove any states are safe, whereas our improvements enable set-based safety analysis. | [
"['Feiyang Cai' 'Chuchu Fan' 'Stanley Bak']"
] |
null | null | 2405.18556 | null | null | http://arxiv.org/pdf/2405.18556v2 | 2024-06-03T20:16:11Z | 2024-05-28T20:03:18Z | Reinforcement Learning in Dynamic Treatment Regimes Needs Critical
Reexamination | In the rapidly changing healthcare landscape, the implementation of offline reinforcement learning (RL) in dynamic treatment regimes (DTRs) presents a mix of unprecedented opportunities and challenges. This position paper offers a critical examination of the current status of offline RL in the context of DTRs. We argue for a reassessment of applying RL in DTRs, citing concerns such as inconsistent and potentially inconclusive evaluation metrics, the absence of naive and supervised learning baselines, and the diverse choice of RL formulation in existing research. Through a case study with more than 17,000 evaluation experiments using a publicly available Sepsis dataset, we demonstrate that the performance of RL algorithms can significantly vary with changes in evaluation metrics and Markov Decision Process (MDP) formulations. Surprisingly, it is observed that in some instances, RL algorithms can be surpassed by random baselines subjected to policy evaluation methods and reward design. This calls for more careful policy evaluation and algorithm development in future DTR works. Additionally, we discussed potential enhancements toward more reliable development of RL-based dynamic treatment regimes and invited further discussion within the community. Code is available at https://github.com/GilesLuo/ReassessDTR. | [
"['Zhiyao Luo' 'Yangchen Pan' 'Peter Watkinson' 'Tingting Zhu']"
] |
null | null | 2405.18560 | null | null | http://arxiv.org/pdf/2405.18560v1 | 2024-05-28T20:10:06Z | 2024-05-28T20:10:06Z | Potential Field Based Deep Metric Learning | Deep metric learning (DML) involves training a network to learn a semantically meaningful representation space. Many current approaches mine n-tuples of examples and model interactions within each tuplets. We present a novel, compositional DML model, inspired by electrostatic fields in physics that, instead of in tuples, represents the influence of each example (embedding) by a continuous potential field, and superposes the fields to obtain their combined global potential field. We use attractive/repulsive potential fields to represent interactions among embeddings from images of the same/different classes. Contrary to typical learning methods, where mutual influence of samples is proportional to their distance, we enforce reduction in such influence with distance, leading to a decaying field. We show that such decay helps improve performance on real world datasets with large intra-class variations and label noise. Like other proxy-based methods, we also use proxies to succinctly represent sub-populations of examples. We evaluate our method on three standard DML benchmarks- Cars-196, CUB-200-2011, and SOP datasets where it outperforms state-of-the-art baselines. | [
"['Shubhang Bhatnagar' 'Narendra Ahuja']"
] |
null | null | 2405.18563 | null | null | http://arxiv.org/pdf/2405.18563v1 | 2024-05-28T20:15:09Z | 2024-05-28T20:15:09Z | Counterfactual Explanations for Multivariate Time-Series without
Training Datasets | Machine learning (ML) methods have experienced significant growth in the past decade, yet their practical application in high-impact real-world domains has been hindered by their opacity. When ML methods are responsible for making critical decisions, stakeholders often require insights into how to alter these decisions. Counterfactual explanations (CFEs) have emerged as a solution, offering interpretations of opaque ML models and providing a pathway to transition from one decision to another. However, most existing CFE methods require access to the model's training dataset, few methods can handle multivariate time-series, and none can handle multivariate time-series without training datasets. These limitations can be formidable in many scenarios. In this paper, we present CFWoT, a novel reinforcement-learning-based CFE method that generates CFEs when training datasets are unavailable. CFWoT is model-agnostic and suitable for both static and multivariate time-series datasets with continuous and discrete features. Users have the flexibility to specify non-actionable, immutable, and preferred features, as well as causal constraints which CFWoT guarantees will be respected. We demonstrate the performance of CFWoT against four baselines on several datasets and find that, despite not having access to a training dataset, CFWoT finds CFEs that make significantly fewer and significantly smaller changes to the input time-series. These properties make CFEs more actionable, as the magnitude of change required to alter an outcome is vastly reduced. | [
"['Xiangyu Sun' 'Raquel Aoki' 'Kevin H. Wilson']"
] |
null | null | 2405.18568 | null | null | http://arxiv.org/pdf/2405.18568v1 | 2024-05-28T20:26:05Z | 2024-05-28T20:26:05Z | Warm-starting Push-Relabel | Push-Relabel is one of the most celebrated network flow algorithms. Maintaining a pre-flow that saturates a cut, it enjoys better theoretical and empirical running time than other flow algorithms, such as Ford-Fulkerson. In practice, Push-Relabel is even faster than what theoretical guarantees can promise, in part because of the use of good heuristics for seeding and updating the iterative algorithm. However, it remains unclear how to run Push-Relabel on an arbitrary initialization that is not necessarily a pre-flow or cut-saturating. We provide the first theoretical guarantees for warm-starting Push-Relabel with a predicted flow, where our learning-augmented version benefits from fast running time when the predicted flow is close to an optimal flow, while maintaining robust worst-case guarantees. Interestingly, our algorithm uses the gap relabeling heuristic, which has long been employed in practice, even though prior to our work there was no rigorous theoretical justification for why it can lead to run-time improvements. We then provide experiments that show our warm-started Push-Relabel also works well in practice. | [
"['Sami Davies' 'Sergei Vassilvitskii' 'Yuyan Wang']"
] |
null | null | 2405.18570 | null | null | http://arxiv.org/pdf/2405.18570v3 | 2024-06-06T20:29:21Z | 2024-05-28T20:28:07Z | It's Not a Modality Gap: Characterizing and Addressing the Contrastive
Gap | Multi-modal contrastive models such as CLIP achieve state-of-the-art performance in zero-shot classification by embedding input images and texts on a joint representational space. Recently, a modality gap has been reported in two-encoder contrastive models like CLIP, meaning that the image and text embeddings reside in disjoint areas of the latent space. Previous studies suggest that this gap exists due to 1) the cone effect, 2) mismatched pairs in the dataset, and 3) insufficient training. We show that, even when accounting for all these factors, and even when using the same modality, the contrastive loss actually creates a gap during training. As a result, We propose that the modality gap is inherent to the two-encoder contrastive loss and rename it the contrastive gap. We present evidence that attributes this contrastive gap to low uniformity in CLIP space, resulting in embeddings that occupy only a small portion of the latent space. To close the gap, we adapt the uniformity and alignment properties of unimodal contrastive loss to the multi-modal setting and show that simply adding these terms to the CLIP loss distributes the embeddings more uniformly in the representational space, closing the gap. In our experiments, we show that the modified representational space achieves better performance than default CLIP loss in downstream tasks such as zero-shot image classification and multi-modal arithmetic. | [
"['Abrar Fahim' 'Alex Murphy' 'Alona Fyshe']"
] |
null | null | 2405.18572 | null | null | http://arxiv.org/pdf/2405.18572v1 | 2024-05-28T20:43:53Z | 2024-05-28T20:43:53Z | Low-rank finetuning for LLMs: A fairness perspective | Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models (LLMs) due to their reduced computational and memory requirements. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. Our findings reveal that there are cases in which low-rank fine-tuning falls short in learning such shifts. This, in turn, produces non-negligible side effects, especially when fine-tuning is adopted for toxicity mitigation in pre-trained models, or in scenarios where it is important to provide fair models. Through comprehensive empirical evidence on several models, datasets, and tasks, we show that low-rank fine-tuning inadvertently preserves undesirable biases and toxic behaviors. We also show that this extends to sequential decision-making tasks, emphasizing the need for careful evaluation to promote responsible LLMs development. | [
"['Saswat Das' 'Marco Romanelli' 'Cuong Tran' 'Zarreen Reza'\n 'Bhavya Kailkhura' 'Ferdinando Fioretto']"
] |
null | null | 2405.18577 | null | null | http://arxiv.org/pdf/2405.18577v2 | 2024-05-30T03:46:44Z | 2024-05-28T20:52:46Z | Single-loop Stochastic Algorithms for Difference of Max-Structured
Weakly Convex Functions | In this paper, we study a class of non-smooth non-convex problems in the form of $min_{x}[max_{yin Y}phi(x, y) - max_{zin Z}psi(x, z)]$, where both $Phi(x) = max_{yin Y}phi(x, y)$ and $Psi(x)=max_{zin Z}psi(x, z)$ are weakly convex functions, and $phi(x, y), psi(x, z)$ are strongly concave functions in terms of $y$ and $z$, respectively. It covers two families of problems that have been studied but are missing single-loop stochastic algorithms, i.e., difference of weakly convex functions and weakly convex strongly-concave min-max problems. We propose a stochastic Moreau envelope approximate gradient method dubbed SMAG, the first single-loop algorithm for solving these problems, and provide a state-of-the-art non-asymptotic convergence rate. The key idea of the design is to compute an approximate gradient of the Moreau envelopes of $Phi, Psi$ using only one step of stochastic gradient update of the primal and dual variables. Empirically, we conduct experiments on positive-unlabeled (PU) learning and partial area under ROC curve (pAUC) optimization with an adversarial fairness regularizer to validate the effectiveness of our proposed algorithms. | [
"['Quanqi Hu' 'Qi Qi' 'Zhaosong Lu' 'Tianbao Yang']"
] |
null | null | 2405.18580 | null | null | http://arxiv.org/pdf/2405.18580v2 | 2024-07-05T08:02:59Z | 2024-05-28T20:54:41Z | Artificial Intelligence in Industry 4.0: A Review of Integration
Challenges for Industrial Systems | In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges. | [
"['Alexander Windmann' 'Philipp Wittenberg' 'Marvin Schieseck'\n 'Oliver Niggemann']"
] |
null | null | 2405.18590 | null | null | http://arxiv.org/pdf/2405.18590v1 | 2024-05-28T21:08:58Z | 2024-05-28T21:08:58Z | A Margin-based Multiclass Generalization Bound via Geometric Complexity | There has been considerable effort to better understand the generalization capabilities of deep neural networks both as a means to unlock a theoretical understanding of their success as well as providing directions for further improvements. In this paper, we investigate margin-based multiclass generalization bounds for neural networks which rely on a recent complexity measure, the geometric complexity, developed for neural networks. We derive a new upper bound on the generalization error which scales with the margin-normalized geometric complexity of the network and which holds for a broad family of data distributions and model classes. Our generalization bound is empirically investigated for a ResNet-18 model trained with SGD on the CIFAR-10 and CIFAR-100 datasets with both original and random labels. | [
"['Michael Munn' 'Benoit Dherin' 'Javier Gonzalvo']"
] |
null | null | 2405.18601 | null | null | http://arxiv.org/pdf/2405.18601v1 | 2024-05-28T21:33:12Z | 2024-05-28T21:33:12Z | From Conformal Predictions to Confidence Regions | Conformal prediction methodologies have significantly advanced the quantification of uncertainties in predictive models. Yet, the construction of confidence regions for model parameters presents a notable challenge, often necessitating stringent assumptions regarding data distribution or merely providing asymptotic guarantees. We introduce a novel approach termed CCR, which employs a combination of conformal prediction intervals for the model outputs to establish confidence regions for model parameters. We present coverage guarantees under minimal assumptions on noise and that is valid in finite sample regime. Our approach is applicable to both split conformal predictions and black-box methodologies including full or cross-conformal approaches. In the specific case of linear models, the derived confidence region manifests as the feasible set of a Mixed-Integer Linear Program (MILP), facilitating the deduction of confidence intervals for individual parameters and enabling robust optimization. We empirically compare CCR to recent advancements in challenging settings such as with heteroskedastic and non-Gaussian noise. | [
"['Charles Guille-Escuret' 'Eugene Ndiaye']"
] |
null | null | 2405.18610 | null | null | http://arxiv.org/pdf/2405.18610v1 | 2024-05-28T21:40:00Z | 2024-05-28T21:40:00Z | DTR-Bench: An in silico Environment and Benchmark Platform for
Reinforcement Learning Based Dynamic Treatment Regime | Reinforcement learning (RL) has garnered increasing recognition for its potential to optimise dynamic treatment regimes (DTRs) in personalised medicine, particularly for drug dosage prescriptions and medication recommendations. However, a significant challenge persists: the absence of a unified framework for simulating diverse healthcare scenarios and a comprehensive analysis to benchmark the effectiveness of RL algorithms within these contexts. To address this gap, we introduce textit{DTR-Bench}, a benchmarking platform comprising four distinct simulation environments tailored to common DTR applications, including cancer chemotherapy, radiotherapy, glucose management in diabetes, and sepsis treatment. We evaluate various state-of-the-art RL algorithms across these settings, particularly highlighting their performance amidst real-world challenges such as pharmacokinetic/pharmacodynamic (PK/PD) variability, noise, and missing data. Our experiments reveal varying degrees of performance degradation among RL algorithms in the presence of noise and patient variability, with some algorithms failing to converge. Additionally, we observe that using temporal observation representations does not consistently lead to improved performance in DTR settings. Our findings underscore the necessity of developing robust, adaptive RL algorithms capable of effectively managing these complexities to enhance patient-specific healthcare. We have open-sourced our benchmark and code at https://github.com/GilesLuo/DTR-Bench. | [
"['Zhiyao Luo' 'Mingcheng Zhu' 'Fenglin Liu' 'Jiali Li' 'Yangchen Pan'\n 'Jiandong Zhou' 'Tingting Zhu']"
] |
null | null | 2405.18613 | null | null | http://arxiv.org/pdf/2405.18613v1 | 2024-05-28T21:42:35Z | 2024-05-28T21:42:35Z | GLOCON Database: Design Decisions and User Manual (v1.0) | GLOCON is a database of contentious events automatically extracted from national news sources from various countries in multiple languages. National news sources are utilized, and complete news archives are processed to create an event list for each source. Automation is achieved using a gold standard corpus sampled randomly from complete news archives (Y"or"uk et al. 2022) and all annotated by at least two domain experts based on the event definition provided in Duruc{s}an et al. (2022). | [
"['Ali Hürriyetoğlu' 'Osman Mutlu' 'Fırat Duruşan' 'Erdem Yörük']"
] |
null | null | 2405.18614 | null | null | http://arxiv.org/pdf/2405.18614v1 | 2024-05-28T21:45:20Z | 2024-05-28T21:45:20Z | Augmented Physics: A Machine Learning-Powered Tool for Creating
Interactive Physics Simulations from Static Diagrams | We introduce Augmented Physics, a machine learning-powered tool designed for creating interactive physics simulations from static textbook diagrams. Leveraging computer vision techniques, such as Segment Anything and OpenCV, our web-based system enables users to semi-automatically extract diagrams from physics textbooks and then generate interactive simulations based on the extracted content. These interactive diagrams are seamlessly integrated into scanned textbook pages, facilitating interactive and personalized learning experiences across various physics concepts, including gravity, optics, circuits, and kinematics. Drawing on an elicitation study with seven physics instructors, we explore four key augmentation techniques: 1) augmented experiments, 2) animated diagrams, 3) bi-directional manipulatives, and 4) parameter visualization. We evaluate our system through technical evaluation, a usability study (N=12), and expert interviews (N=12). The study findings suggest that our system can facilitate more engaging and personalized learning experiences in physics education. | [
"['Aditya Gunturu' 'Yi Wen' 'Jarin Thundathil' 'Nandi Zhang'\n 'Rubaiat Habib Kazi' 'Ryo Suzuki']"
] |
null | null | 2405.18621 | null | null | http://arxiv.org/pdf/2405.18621v1 | 2024-05-28T22:01:50Z | 2024-05-28T22:01:50Z | Multi-Armed Bandits with Network Interference | Online experimentation with interference is a common challenge in modern applications such as e-commerce and adaptive clinical trials in medicine. For example, in online marketplaces, the revenue of a good depends on discounts applied to competing goods. Statistical inference with interference is widely studied in the offline setting, but far less is known about how to adaptively assign treatments to minimize regret. We address this gap by studying a multi-armed bandit (MAB) problem where a learner (e-commerce platform) sequentially assigns one of possible $mathcal{A}$ actions (discounts) to $N$ units (goods) over $T$ rounds to minimize regret (maximize revenue). Unlike traditional MAB problems, the reward of each unit depends on the treatments assigned to other units, i.e., there is interference across the underlying network of units. With $mathcal{A}$ actions and $N$ units, minimizing regret is combinatorially difficult since the action space grows as $mathcal{A}^N$. To overcome this issue, we study a sparse network interference model, where the reward of a unit is only affected by the treatments assigned to $s$ neighboring units. We use tools from discrete Fourier analysis to develop a sparse linear representation of the unit-specific reward $r_n: [mathcal{A}]^N rightarrow mathbb{R} $, and propose simple, linear regression-based algorithms to minimize regret. Importantly, our algorithms achieve provably low regret both when the learner observes the interference neighborhood for all units and when it is unknown. This significantly generalizes other works on this topic which impose strict conditions on the strength of interference on a known network, and also compare regret to a markedly weaker optimal action. Empirically, we corroborate our theoretical findings via numerical simulations. | [
"['Abhineet Agarwal' 'Anish Agarwal' 'Lorenzo Masoero' 'Justin Whitehouse']"
] |
null | null | 2405.18622 | null | null | http://arxiv.org/pdf/2405.18622v1 | 2024-05-28T22:04:29Z | 2024-05-28T22:04:29Z | Biclustering a dataset using photonic quantum computing | Biclustering is a problem in machine learning and data mining that seeks to group together rows and columns of a dataset according to certain criteria. In this work, we highlight the natural relation that quantum computing models like boson and Gaussian boson sampling (GBS) have to this problem. We first explore the use of boson sampling to identify biclusters based on matrix permanents. We then propose a heuristic that finds clusters in a dataset using Gaussian boson sampling by (i) converting the dataset into a bipartite graph and then (ii) running GBS to find the densest sub-graph(s) within the larger bipartite graph. Our simulations for the above proposed heuristics show promising results for future exploration in this area. | [
"['Ajinkya Borle' 'Ameya Bhave']"
] |
null | null | 2405.18626 | null | null | http://arxiv.org/pdf/2405.18626v2 | 2024-06-02T13:54:06Z | 2024-05-28T22:17:57Z | Causal Contextual Bandits with Adaptive Context | We study a variant of causal contextual bandits where the context is chosen based on an initial intervention chosen by the learner. At the beginning of each round, the learner selects an initial action, depending on which a stochastic context is revealed by the environment. Following this, the learner then selects a final action and receives a reward. Given $T$ rounds of interactions with the environment, the objective of the learner is to learn a policy (of selecting the initial and the final action) with maximum expected reward. In this paper we study the specific situation where every action corresponds to intervening on a node in some known causal graph. We extend prior work from the deterministic context setting to obtain simple regret minimization guarantees. This is achieved through an instance-dependent causal parameter, $lambda$, which characterizes our upper bound. Furthermore, we prove that our simple regret is essentially tight for a large class of instances. A key feature of our work is that we use convex optimization to address the bandit exploration problem. We also conduct experiments to validate our theoretical results, and release our code at our project GitHub repository: https://github.com/adaptiveContextualCausalBandits/aCCB. | [
"['Rahul Madhavan' 'Aurghya Maiti' 'Gaurav Sinha' 'Siddharth Barman']"
] |
null | null | 2405.18627 | null | null | http://arxiv.org/pdf/2405.18627v2 | 2024-06-02T20:11:50Z | 2024-05-28T22:19:26Z | PureGen: Universal Data Purification for Train-Time Poison Defense via
Generative Model Dynamics | Train-time data poisoning attacks threaten machine learning models by introducing adversarial examples during training, leading to misclassification. Current defense methods often reduce generalization performance, are attack-specific, and impose significant training overhead. To address this, we introduce a set of universal data purification methods using a stochastic transform, $Psi(x)$, realized via iterative Langevin dynamics of Energy-Based Models (EBMs), Denoising Diffusion Probabilistic Models (DDPMs), or both. These approaches purify poisoned data with minimal impact on classifier generalization. Our specially trained EBMs and DDPMs provide state-of-the-art defense against various attacks (including Narcissus, Bullseye Polytope, Gradient Matching) on CIFAR-10, Tiny-ImageNet, and CINIC-10, without needing attack or classifier-specific information. We discuss performance trade-offs and show that our methods remain highly effective even with poisoned or distributionally shifted generative model training data. | [
"['Sunay Bhat' 'Jeffrey Jiang' 'Omead Pooladzandi' 'Alexander Branch'\n 'Gregory Pottie']"
] |
null | null | 2405.18628 | null | null | http://arxiv.org/pdf/2405.18628v2 | 2024-06-02T14:58:48Z | 2024-05-28T22:19:30Z | Hardware-Aware Parallel Prompt Decoding for Memory-Efficient
Acceleration of LLM Inference | The auto-regressive decoding of Large Language Models (LLMs) results in significant overheads in their hardware performance. While recent research has investigated various speculative decoding techniques for multi-token generation, these efforts have primarily focused on improving processing speed such as throughput. Crucially, they often neglect other metrics essential for real-life deployments, such as memory consumption and training cost. To overcome these limitations, we propose a novel parallel prompt decoding that requires only $0.0002$% trainable parameters, enabling efficient training on a single A100-40GB GPU in just 16 hours. Inspired by the human natural language generation process, $PPD$ approximates outputs generated at future timesteps in parallel by using multiple prompt tokens. This approach partially recovers the missing conditional dependency information necessary for multi-token generation, resulting in up to a 28% higher acceptance rate for long-range predictions. Furthermore, we present a hardware-aware dynamic sparse tree technique that adaptively optimizes this decoding scheme to fully leverage the computational capacities on different GPUs. Through extensive experiments across LLMs ranging from MobileLlama to Vicuna-13B on a wide range of benchmarks, our approach demonstrates up to 2.49$times$ speedup and maintains a minimal runtime memory overhead of just $0.0004$%. More importantly, our parallel prompt decoding can serve as an orthogonal optimization for synergistic integration with existing speculative decoding, showing up to $1.22times$ further speed improvement. Our code is available at https://github.com/hmarkc/parallel-prompt-decoding. | [
"['Hao Mark Chen' 'Wayne Luk' 'Ka Fai Cedric Yiu' 'Rui Li'\n 'Konstantin Mishchenko' 'Stylianos I. Venieris' 'Hongxiang Fan']"
] |
null | null | 2405.18634 | null | null | http://arxiv.org/pdf/2405.18634v1 | 2024-05-28T22:33:02Z | 2024-05-28T22:33:02Z | A Theoretical Understanding of Self-Correction through In-context
Alignment | Going beyond mimicking limited human experiences, recent studies show initial evidence that, like humans, large language models (LLMs) are capable of improving their abilities purely by self-correction, i.e., correcting previous responses through self-examination, in certain circumstances. Nevertheless, little is known about how such capabilities arise. In this work, based on a simplified setup akin to an alignment task, we theoretically analyze self-correction from an in-context learning perspective, showing that when LLMs give relatively accurate self-examinations as rewards, they are capable of refining responses in an in-context way. Notably, going beyond previous theories on over-simplified linear transformers, our theoretical construction underpins the roles of several key designs of realistic transformers for self-correction: softmax attention, multi-head attention, and the MLP block. We validate these findings extensively on synthetic datasets. Inspired by these findings, we also illustrate novel applications of self-correction, such as defending against LLM jailbreaks, where a simple self-correction step does make a large difference. We believe that these findings will inspire further research on understanding, exploiting, and enhancing self-correction for building better foundation models. | [
"['Yifei Wang' 'Yuyang Wu' 'Zeming Wei' 'Stefanie Jegelka' 'Yisen Wang']"
] |
null | null | 2405.18635 | null | null | http://arxiv.org/pdf/2405.18635v1 | 2024-05-28T22:34:53Z | 2024-05-28T22:34:53Z | When and How Does In-Distribution Label Help Out-of-Distribution
Detection? | Detecting data points deviating from the training distribution is pivotal for ensuring reliable machine learning. Extensive research has been dedicated to the challenge, spanning classical anomaly detection techniques to contemporary out-of-distribution (OOD) detection approaches. While OOD detection commonly relies on supervised learning from a labeled in-distribution (ID) dataset, anomaly detection may treat the entire ID data as a single class and disregard ID labels. This fundamental distinction raises a significant question that has yet to be rigorously explored: when and how does ID label help OOD detection? This paper bridges this gap by offering a formal understanding to theoretically delineate the impact of ID labels on OOD detection. We employ a graph-theoretic approach, rigorously analyzing the separability of ID data from OOD data in a closed-form manner. Key to our approach is the characterization of data representations through spectral decomposition on the graph. Leveraging these representations, we establish a provable error bound that compares the OOD detection performance with and without ID labels, unveiling conditions for achieving enhanced OOD detection. Lastly, we present empirical results on both simulated and real datasets, validating theoretical guarantees and reinforcing our insights. Code is publicly available at https://github.com/deeplearning-wisc/id_label. | [
"['Xuefeng Du' 'Yiyou Sun' 'Yixuan Li']"
] |
null | null | 2405.18639 | null | null | http://arxiv.org/pdf/2405.18639v1 | 2024-05-28T22:48:53Z | 2024-05-28T22:48:53Z | Improving Speech Decoding from ECoG with Self-Supervised Pretraining | Recent work on intracranial brain-machine interfaces has demonstrated that spoken speech can be decoded with high accuracy, essentially by treating the problem as an instance of supervised learning and training deep neural networks to map from neural activity to text. However, such networks pay for their expressiveness with very large numbers of labeled data, a requirement that is particularly burdensome for invasive neural recordings acquired from human patients. On the other hand, these patients typically produce speech outside of the experimental blocks used for training decoders. Making use of such data, and data from other patients, to improve decoding would ease the burden of data collection -- especially onerous for dys- and anarthric patients. Here we demonstrate that this is possible, by reengineering wav2vec -- a simple, self-supervised, fully convolutional model that learns latent representations of audio using a noise-contrastive loss -- for electrocorticographic (ECoG) data. We train this model on unlabelled ECoG recordings, and subsequently use it to transform ECoG from labeled speech sessions into wav2vec's representation space, before finally training a supervised encoder-decoder to map these representations to text. We experiment with various numbers of labeled blocks; for almost all choices, the new representations yield superior decoding performance to the original ECoG data, and in no cases do they yield worse. Performance can also be improved in some cases by pretraining wav2vec on another patient's data. In the best cases, wav2vec's representations decrease word error rates over the original data by upwards of 50%. | [
"['Brian A. Yuan' 'Joseph G. Makin']"
] |
null | null | 2405.18641 | null | null | http://arxiv.org/pdf/2405.18641v4 | 2024-06-26T18:54:59Z | 2024-05-28T22:53:43Z | Lazy Safety Alignment for Large Language Models against Harmful
Fine-tuning | Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-broken by fine-tuning on a dataset mixed with harmful data. First time in the literature, we show that the jail-broken effect can be mitigated by separating states in the finetuning stage to optimize the alignment and user datasets. Unfortunately, our subsequent study shows that this simple Bi-State Optimization (BSO) solution experiences convergence instability when steps invested in its alignment state is too small, leading to downgraded alignment performance. By statistical analysis, we show that the textit{excess drift} towards consensus could be a probable reason for the instability. To remedy this issue, we propose textbf{L}azy(textbf{i}) textbf{s}afety textbf{a}lignment (textbf{Lisa}), which introduces a proximal term to constraint the drift of each state. Theoretically, the benefit of the proximal term is supported by the convergence analysis, wherein we show that a sufficient large proximal factor is necessary to guarantee Lisa's convergence. Empirically, our results on four downstream finetuning tasks show that Lisa with a proximal term can significantly increase alignment performance while maintaining the LLM's accuracy on the user tasks. Code is available at url{https://github.com/git-disl/Lisa}. | [
"['Tiansheng Huang' 'Sihao Hu' 'Fatih Ilhan' 'Selim Furkan Tekin'\n 'Ling Liu']"
] |
null | null | 2405.18655 | null | null | http://arxiv.org/pdf/2405.18655v1 | 2024-05-28T23:44:09Z | 2024-05-28T23:44:09Z | CAVACHON: a hierarchical variational autoencoder to integrate
multi-modal single-cell data | Paired single-cell sequencing technologies enable the simultaneous measurement of complementary modalities of molecular data at single-cell resolution. Along with the advances in these technologies, many methods based on variational autoencoders have been developed to integrate these data. However, these methods do not explicitly incorporate prior biological relationships between the data modalities, which could significantly enhance modeling and interpretation. We propose a novel probabilistic learning framework that explicitly incorporates conditional independence relationships between multi-modal data as a directed acyclic graph using a generalized hierarchical variational autoencoder. We demonstrate the versatility of our framework across various applications pertinent to single-cell multi-omics data integration. These include the isolation of common and distinct information from different modalities, modality-specific differential analysis, and integrated cell clustering. We anticipate that the proposed framework can facilitate the construction of highly flexible graphical models that can capture the complexities of biological hypotheses and unravel the connections between different biological data types, such as different modalities of paired single-cell multi-omics data. The implementation of the proposed framework can be found in the repository https://github.com/kuijjerlab/CAVACHON. | [
"['Ping-Han Hsieh' 'Ru-Xiu Hsiao' 'Katalin Ferenc' 'Anthony Mathelier'\n 'Rebekka Burkholz' 'Chien-Yu Chen' 'Geir Kjetil Sandve' 'Tatiana Belova'\n 'Marieke Lydia Kuijjer']"
] |
null | null | 2405.18662 | null | null | http://arxiv.org/pdf/2405.18662v1 | 2024-05-28T23:54:44Z | 2024-05-28T23:54:44Z | Understanding Intrinsic Socioeconomic Biases in Large Language Models | Large Language Models (LLMs) are increasingly integrated into critical decision-making processes, such as loan approvals and visa applications, where inherent biases can lead to discriminatory outcomes. In this paper, we examine the nuanced relationship between demographic attributes and socioeconomic biases in LLMs, a crucial yet understudied area of fairness in LLMs. We introduce a novel dataset of one million English sentences to systematically quantify socioeconomic biases across various demographic groups. Our findings reveal pervasive socioeconomic biases in both established models such as GPT-2 and state-of-the-art models like Llama 2 and Falcon. We demonstrate that these biases are significantly amplified when considering intersectionality, with LLMs exhibiting a remarkable capacity to extract multiple demographic attributes from names and then correlate them with specific socioeconomic biases. This research highlights the urgent necessity for proactive and robust bias mitigation techniques to safeguard against discriminatory outcomes when deploying these powerful models in critical real-world applications. | [
"['Mina Arzaghi' 'Florian Carichon' 'Golnoosh Farnadi']"
] |
null | null | 2405.18664 | null | null | http://arxiv.org/pdf/2405.18664v1 | 2024-05-29T00:01:40Z | 2024-05-29T00:01:40Z | Fast Explainability via Feasible Concept Sets Generator | A long-standing dilemma prevents the broader application of explanation methods: general applicability and inference speed. On the one hand, existing model-agnostic explanation methods usually make minimal pre-assumptions about the prediction models to be explained. Still, they require additional queries to the model through propagation or back-propagation to approximate the models' behaviors, resulting in slow inference and hindering their use in time-sensitive tasks. On the other hand, various model-dependent explanations have been proposed that achieve low-cost, fast inference but at the expense of limiting their applicability to specific model structures. In this study, we bridge the gap between the universality of model-agnostic approaches and the efficiency of model-specific approaches by proposing a novel framework without assumptions on the prediction model's structures, achieving high efficiency during inference and allowing for real-time explanations. To achieve this, we first define explanations through a set of human-comprehensible concepts and propose a framework to elucidate model predictions via minimal feasible concept sets. Second, we show that a minimal feasible set generator can be learned as a companion explainer to the prediction model, generating explanations for predictions. Finally, we validate this framework by implementing a novel model-agnostic method that provides robust explanations while facilitating real-time inference. Our claims are substantiated by comprehensive experiments, highlighting the effectiveness and efficiency of our approach. | [
"['Deng Pan' 'Nuno Moniz' 'Nitesh Chawla']"
] |
null | null | 2405.18669 | null | null | http://arxiv.org/pdf/2405.18669v2 | 2024-05-31T15:42:53Z | 2024-05-29T00:23:55Z | Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities | Integrating multiple generative foundation models, especially those trained on different modalities, into something greater than the sum of its parts poses significant challenges. Two key hurdles are the availability of aligned data (concepts that contain similar meaning but is expressed differently in different modalities), and effectively leveraging unimodal representations in cross-domain generative tasks, without compromising their original unimodal capabilities. We propose Zipper, a multi-tower decoder architecture that addresses these concerns by using cross-attention to flexibly compose multimodal generative models from independently pre-trained unimodal decoders. In our experiments fusing speech and text modalities, we show the proposed architecture performs very competitively in scenarios with limited aligned text-speech data. We also showcase the flexibility of our model to selectively maintain unimodal (e.g., text-to-text generation) generation performance by freezing the corresponding modal tower (e.g. text). In cross-modal tasks such as automatic speech recognition (ASR) where the output modality is text, we show that freezing the text backbone results in negligible performance degradation. In cross-modal tasks such as text-to-speech generation (TTS) where the output modality is speech, we show that using a pre-trained speech backbone results in superior performance to the baseline. | [
"['Vicky Zayats' 'Peter Chen' 'Melissa Ferrari' 'Dirk Padfield']"
] |
null | null | 2405.18670 | null | null | http://arxiv.org/pdf/2405.18670v1 | 2024-05-29T00:25:07Z | 2024-05-29T00:25:07Z | Adapting Differentially Private Synthetic Data to Relational Databases | Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with relationships across tables. In this paper, we introduce the first-of-its-kind algorithm that can be combined with any existing DP mechanisms to generate synthetic relational databases. Our algorithm iteratively refines the relationship between individual synthetic tables to minimize their approximation errors in terms of low-order marginal distributions while maintaining referential integrity. Finally, we provide both DP and theoretical utility guarantees for our algorithm. | [
"['Kaveh Alimohammadi' 'Hao Wang' 'Ojas Gulati' 'Akash Srivastava'\n 'Navid Azizan']"
] |
null | null | 2405.18671 | null | null | http://arxiv.org/pdf/2405.18671v1 | 2024-05-29T00:33:56Z | 2024-05-29T00:33:56Z | Watermarking Counterfactual Explanations | The field of Explainable Artificial Intelligence (XAI) focuses on techniques for providing explanations to end-users about the decision-making processes that underlie modern-day machine learning (ML) models. Within the vast universe of XAI techniques, counterfactual (CF) explanations are often preferred by end-users as they help explain the predictions of ML models by providing an easy-to-understand & actionable recourse (or contrastive) case to individual end-users who are adversely impacted by predicted outcomes. However, recent studies have shown significant security concerns with using CF explanations in real-world applications; in particular, malicious adversaries can exploit CF explanations to perform query-efficient model extraction attacks on proprietary ML models. In this paper, we propose a model-agnostic watermarking framework (for adding watermarks to CF explanations) that can be leveraged to detect unauthorized model extraction attacks (which rely on the watermarked CF explanations). Our novel framework solves a bi-level optimization problem to embed an indistinguishable watermark into the generated CF explanation such that any future model extraction attacks that rely on these watermarked CF explanations can be detected using a null hypothesis significance testing (NHST) scheme, while ensuring that these embedded watermarks do not compromise the quality of the generated CF explanations. We evaluate this framework's performance across a diverse set of real-world datasets, CF explanation methods, and model extraction techniques, and show that our watermarking detection system can be used to accurately identify extracted ML models that are trained using the watermarked CF explanations. Our work paves the way for the secure adoption of CF explanations in real-world applications. | [
"['Hangzhi Guo' 'Amulya Yadav']"
] |
null | null | 2405.18674 | null | null | http://arxiv.org/pdf/2405.18674v1 | 2024-05-29T00:42:00Z | 2024-05-29T00:42:00Z | Deep Bayesian Filter for Bayes-faithful Data Assimilation | State estimation for nonlinear state space models is a challenging task. Existing assimilation methodologies predominantly assume Gaussian posteriors on physical space, where true posteriors become inevitably non-Gaussian. We propose Deep Bayesian Filtering (DBF) for data assimilation on nonlinear state space models (SSMs). DBF constructs new latent variables $h_t$ on a new latent (``fancy'') space and assimilates observations $o_t$. By (i) constraining the state transition on fancy space to be linear and (ii) learning a Gaussian inverse observation operator $q(h_t|o_t)$, posteriors always remain Gaussian for DBF. Quite distinctively, the structured design of posteriors provides an analytic formula for the recursive computation of posteriors without accumulating Monte-Carlo sampling errors over time steps. DBF seeks the Gaussian inverse observation operators $q(h_t|o_t)$ and other latent SSM parameters (e.g., dynamics matrix) by maximizing the evidence lower bound. Experiments show that DBF outperforms model-based approaches and latent assimilation methods in various tasks and conditions. | [
"['Yuta Tarumi' 'Keisuke Fukuda' 'Shin-ichi Maeda']"
] |
null | null | 2405.18680 | null | null | http://arxiv.org/pdf/2405.18680v1 | 2024-05-29T01:07:26Z | 2024-05-29T01:07:26Z | Navigable Graphs for High-Dimensional Nearest Neighbor Search:
Constructions and Limits | There has been significant recent interest in graph-based nearest neighbor search methods, many of which are centered on the construction of navigable graphs over high-dimensional point sets. A graph is navigable if we can successfully move from any starting node to any target node using a greedy routing strategy where we always move to the neighbor that is closest to the destination according to a given distance function. The complete graph is navigable for any point set, but the important question for applications is if sparser graphs can be constructed. While this question is fairly well understood in low-dimensions, we establish some of the first upper and lower bounds for high-dimensional point sets. First, we give a simple and efficient way to construct a navigable graph with average degree $O(sqrt{n log n })$ for any set of $n$ points, in any dimension, for any distance function. We compliment this result with a nearly matching lower bound: even under the Euclidean metric in $O(log n)$ dimensions, a random point set has no navigable graph with average degree $O(n^{alpha})$ for any $alpha < 1/2$. Our lower bound relies on sharp anti-concentration bounds for binomial random variables, which we use to show that the near-neighborhoods of a set of random points do not overlap significantly, forcing any navigable graph to have many edges. | [
"['Haya Diwan' 'Jinrui Gou' 'Cameron Musco' 'Christopher Musco'\n 'Torsten Suel']"
] |
null | null | 2405.18682 | null | null | http://arxiv.org/pdf/2405.18682v1 | 2024-05-29T01:12:53Z | 2024-05-29T01:12:53Z | Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical
Machine Reading Comprehension | Large language models (LLMs) have shown remarkable performance on many tasks in different domains. However, their performance in closed-book biomedical machine reading comprehension (MRC) has not been evaluated in depth. In this work, we evaluate GPT on four closed-book biomedical MRC benchmarks. We experiment with different conventional prompting techniques as well as introduce our own novel prompting method. To solve some of the retrieval problems inherent to LLMs, we propose a prompting strategy named Implicit Retrieval Augmented Generation (RAG) that alleviates the need for using vector databases to retrieve important chunks in traditional RAG setups. Moreover, we report qualitative assessments on the natural language generation outputs from our approach. The results show that our new prompting technique is able to get the best performance in two out of four datasets and ranks second in rest of them. Experiments show that modern-day LLMs like GPT even in a zero-shot setting can outperform supervised models, leading to new state-of-the-art (SoTA) results on two of the benchmarks. | [
"['Shubham Vatsal' 'Ayush Singh']"
] |
null | null | 2405.18686 | null | null | http://arxiv.org/pdf/2405.18686v1 | 2024-05-29T01:32:17Z | 2024-05-29T01:32:17Z | Rejection via Learning Density Ratios | Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. The predominant approach is to alter the supervised learning pipeline by augmenting typical loss functions, letting model rejection incur a lower loss than an incorrect prediction. Instead, we propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. This can be formalized via the optimization of a loss's risk with a $ phi$-divergence regularization term. Through this idealized distribution, a rejection decision can be made by utilizing the density ratio between this distribution and the data distribution. We focus on the setting where our $ phi $-divergences are specified by the family of $ alpha $-divergence. Our framework is tested empirically over clean and noisy datasets. | [
"['Alexander Soen' 'Hisham Husain' 'Philip Schulz' 'Vu Nguyen']"
] |
null | null | 2405.18687 | null | null | http://arxiv.org/abs/2405.18687v1 | 2024-05-29T01:46:50Z | 2024-05-29T01:46:50Z | Advancing Household Robotics: Deep Interactive Reinforcement Learning
for Efficient Training and Enhanced Performance | The market for domestic robots made to perform household chores is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labor, in contrast to industrial robots, which are frequently criticized for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in several minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviors. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to optimize their actions to maximize rewards. However, the goal of Deep Reinforcement Learning is to address more complicated, continuous action-state spaces in real-world settings by combining RL with Neural Networks. The efficacy of DeepRL can be further augmented through interactive feedback, in which a trainer offers real-time guidance to expedite the robot's learning process. Nevertheless, the current methods have drawbacks, namely the transient application of guidance that results in repeated learning under identical conditions. Therefore, we present a novel method to preserve and reuse information and advice via Deep Interactive Reinforcement Learning, which utilizes a persistent rule-based system. This method not only expedites the training process but also lessens the number of repetitions that instructors will have to carry out. This study has the potential to advance the development of household robots and improve their effectiveness and efficiency as learners. | [
"['Arpita Soni' 'Sujatha Alla' 'Suresh Dodda' 'Hemanth Volikatla']"
] |
null | null | 2405.18688 | null | null | http://arxiv.org/pdf/2405.18688v1 | 2024-05-29T01:49:20Z | 2024-05-29T01:49:20Z | Efficient Preference-based Reinforcement Learning via Aligned Experience
Estimation | Preference-based reinforcement learning (PbRL) has shown impressive capabilities in training agents without reward engineering. However, a notable limitation of PbRL is its dependency on substantial human feedback. This dependency stems from the learning loop, which entails accurate reward learning compounded with value/policy learning, necessitating a considerable number of samples. To boost the learning loop, we propose SEER, an efficient PbRL method that integrates label smoothing and policy regularization techniques. Label smoothing reduces overfitting of the reward model by smoothing human preference labels. Additionally, we bootstrap a conservative estimate $widehat{Q}$ using well-supported state-action pairs from the current replay memory to mitigate overestimation bias and utilize it for policy learning regularization. Our experimental results across a variety of complex tasks, both in online and offline settings, demonstrate that our approach improves feedback efficiency, outperforming state-of-the-art methods by a large margin. Ablation studies further reveal that SEER achieves a more accurate Q-function compared to prior work. | [
"['Fengshuo Bai' 'Rui Zhao' 'Hongming Zhang' 'Sijia Cui' 'Ying Wen'\n 'Yaodong Yang' 'Bo Xu' 'Lei Han']"
] |
null | null | 2405.18693 | null | null | http://arxiv.org/pdf/2405.18693v1 | 2024-05-29T02:06:17Z | 2024-05-29T02:06:17Z | DeepHGNN: Study of Graph Neural Network based Forecasting Methods for
Hierarchically Related Multivariate Time Series | Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships. This paper introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed for forecasting in complex hierarchical structures. The uniqueness of DeepHGNN lies in its innovative graph-based hierarchical interpolation and an end-to-end reconciliation mechanism. This approach ensures forecast accuracy and coherence across various hierarchical levels while sharing signals across them, addressing a key challenge in hierarchical forecasting. A critical insight in hierarchical time series is the variance in forecastability across levels, with upper levels typically presenting more predictable components. DeepHGNN capitalizes on this insight by pooling and leveraging knowledge from all hierarchy levels, thereby enhancing the overall forecast accuracy. Our comprehensive evaluation set against several state-of-the-art models confirm the superior performance of DeepHGNN. This research not only demonstrates DeepHGNN's effectiveness in achieving significantly improved forecast accuracy but also contributes to the understanding of graph-based methods in hierarchical time series forecasting. | [
"['Abishek Sriramulu' 'Nicolas Fourrier' 'Christoph Bergmeir']"
] |
null | null | 2405.18698 | null | null | http://arxiv.org/pdf/2405.18698v1 | 2024-05-29T02:17:25Z | 2024-05-29T02:17:25Z | Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees | The field of risk-constrained reinforcement learning (RCRL) has been developed to effectively reduce the likelihood of worst-case scenarios by explicitly handling risk-measure-based constraints. However, the nonlinearity of risk measures makes it challenging to achieve convergence and optimality. To overcome the difficulties posed by the nonlinearity, we propose a spectral risk measure-constrained RL algorithm, spectral-risk-constrained policy optimization (SRCPO), a bilevel optimization approach that utilizes the duality of spectral risk measures. In the bilevel optimization structure, the outer problem involves optimizing dual variables derived from the risk measures, while the inner problem involves finding an optimal policy given these dual variables. The proposed method, to the best of our knowledge, is the first to guarantee convergence to an optimum in the tabular setting. Furthermore, the proposed method has been evaluated on continuous control tasks and showed the best performance among other RCRL algorithms satisfying the constraints. | [
"['Dohyeong Kim' 'Taehyun Cho' 'Seungyub Han' 'Hojun Chung' 'Kyungjae Lee'\n 'Songhwai Oh']"
] |
null | null | 2405.18707 | null | null | http://arxiv.org/pdf/2405.18707v1 | 2024-05-29T02:34:38Z | 2024-05-29T02:34:38Z | Adaptive and Parallel Split Federated Learning in Vehicular Edge
Computing | Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent transportation systems by accommodating artificial intelligence (AI) at the vehicular edge computing (VEC) system. Federated learning (FL) stands as one of the fundamental technologies facilitating collaborative model training locally and aggregation, while safeguarding the privacy of vehicle data in VEI. However, traditional FL faces challenges in adapting to vehicle heterogeneity, training large models on resource-constrained vehicles, and remaining susceptible to model weight privacy leakage. Meanwhile, split learning (SL) is proposed as a promising collaborative learning framework which can mitigate the risk of model wights leakage, and release the training workload on vehicles. SL sequentially trains a model between a vehicle and an edge cloud (EC) by dividing the entire model into a vehicle-side model and an EC-side model at a given cut layer. In this work, we combine the advantages of SL and FL to develop an Adaptive Split Federated Learning scheme for Vehicular Edge Computing (ASFV). The ASFV scheme adaptively splits the model and parallelizes the training process, taking into account mobile vehicle selection and resource allocation. Our extensive simulations, conducted on non-independent and identically distributed data, demonstrate that the proposed ASFV solution significantly reduces training latency compared to existing benchmarks, while adapting to network dynamics and vehicles' mobility. | [
"['Xianke Qiang' 'Zheng Chang' 'Yun Hu' 'Lei Liu' 'Timo Hamalainen']"
] |
null | null | 2405.18710 | null | null | http://arxiv.org/pdf/2405.18710v1 | 2024-05-29T02:42:23Z | 2024-05-29T02:42:23Z | To FP8 and Back Again: Quantifying the Effects of Reducing Precision on
LLM Training Stability | The massive computational costs associated with large language model (LLM) pretraining have spurred great interest in reduced-precision floating-point representations to accelerate the process. As a result, the BrainFloat16 (BF16) precision has become the de facto standard for LLM training, with hardware support included in recent accelerators. This trend has gone even further in the latest processors, where FP8 has recently been introduced. However, prior experience with FP16, which was found to be less stable than BF16, raises concerns as to whether FP8, with even fewer bits than FP16, can be a cost-effective option for LLM training. We argue that reduced-precision training schemes must have similar training stability and hyperparameter sensitivities to their higher-precision counterparts in order to be cost-effective. However, we find that currently available methods for FP8 training are not robust enough to allow their use as economical replacements. This prompts us to investigate the stability of reduced-precision LLM training in terms of robustness across random seeds and learning rates. To this end, we propose new evaluation techniques and a new metric for quantifying loss landscape sharpness in autoregressive language models. By simulating incremental bit reductions in floating-point representations, we analyze the relationship between representational power and training stability with the intent of aiding future research into the field. | [
"['Joonhyung Lee' 'Jeongin Bae' 'Byeongwook Kim' 'Se Jung Kwon'\n 'Dongsoo Lee']"
] |
null | null | 2405.18723 | null | null | http://arxiv.org/pdf/2405.18723v2 | 2024-06-30T17:01:51Z | 2024-05-29T03:08:30Z | Conformal Depression Prediction | While existing depression prediction methods based on deep learning show promise, their practical application is hindered by the lack of trustworthiness, as these deep models are often deployed as textit{black box} models, leaving us uncertain about the confidence of the model predictions. For high-risk clinical applications like depression prediction, uncertainty quantification is essential in decision-making. In this paper, we introduce conformal depression prediction (CDP), a depression prediction method with uncertainty quantification based on conformal prediction (CP), giving valid confidence intervals with theoretical coverage guarantees for the model predictions. CDP is a plug-and-play module that requires neither model retraining nor an assumption about the depression data distribution. As CDP provides only an average coverage guarantee across all inputs rather than per-input performance guarantee, we further propose CDP-ACC, an improved conformal prediction with approximate conditional coverage. CDP-ACC firstly estimates the prediction distribution through neighborhood relaxation, and then introduces a conformal score function by constructing nested sequences, so as to provide a tighter prediction interval for each specific input. We empirically demonstrate the application of CDP in uncertainty-aware depression prediction, as well as the effectiveness and superiority of CDP-ACC on the AVEC 2013 and AVEC 2014 datasets. | [
"['Yonghong Li' 'Shan Qu' 'Xiuzhuang Zhou']"
] |
null | null | 2405.18724 | null | null | http://arxiv.org/pdf/2405.18724v1 | 2024-05-29T03:10:21Z | 2024-05-29T03:10:21Z | Adapting Differential Molecular Representation with Hierarchical Prompts
for Multi-label Property Prediction | Accurate prediction of molecular properties is critical in the field of drug discovery. However, existing methods do not fully consider the fact that molecules in the real world usually possess multiple property labels, and complex high-order relationships may exist among these labels. Therefore, molecular representation learning models should generate differential molecular representations that consider multi-granularity correlation information among tasks. To this end, our research introduces a Hierarchical Prompted Molecular Representation Learning Framework (HiPM), which enhances the differential expression of tasks in molecular representations through task-aware prompts, and utilizes shared information among labels to mitigate negative transfer between different tasks. HiPM primarily consists of two core components: the Molecular Representation Encoder (MRE) and the Task-Aware Prompter (TAP). The MRE employs a hierarchical message-passing network architecture to capture molecular features at both the atomic and motif levels, while the TAP uses agglomerative hierarchical clustering to build a prompt tree that reflects the affinity and distinctiveness of tasks, enabling the model to effectively handle the complexity of multi-label property predictions. Extensive experiments demonstrate that HiPM achieves state-of-the-art performance across various multi-label datasets, offering a new perspective on multi-label molecular representation learning. | [
"['Linjia Kang' 'Songhua Zhou' 'Shuyan Fang' 'Shichao Liu' 'Wen Zhang']"
] |
null | null | 2405.18725 | null | null | http://arxiv.org/pdf/2405.18725v1 | 2024-05-29T03:16:12Z | 2024-05-29T03:16:12Z | Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground
Truth? | Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is required to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatial-temporal transformer network to predict the ground truth of the input sensing data. Then, we extract the sensing errors of the data as features based on the prediction results to calculate the implications among the data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that PRBTD outperforms the existing methods in terms of identification accuracy and data quality enhancement. | [
"['Jiajie Li' 'Bo Gu' 'Shimin Gong' 'Zhou Su' 'Mohsen Guizani']"
] |
null | null | 2405.18729 | null | null | http://arxiv.org/pdf/2405.18729v1 | 2024-05-29T03:19:59Z | 2024-05-29T03:19:59Z | Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement
Learning | Offline reinforcement learning (RL) aims to learn optimal policies from previously collected datasets. Recently, due to their powerful representational capabilities, diffusion models have shown significant potential as policy models for offline RL issues. However, previous offline RL algorithms based on diffusion policies generally adopt weighted regression to improve the policy. This approach optimizes the policy only using the collected actions and is sensitive to Q-values, which limits the potential for further performance enhancement. To this end, we propose a novel preferred-action-optimized diffusion policy for offline RL. In particular, an expressive conditional diffusion model is utilized to represent the diverse distribution of a behavior policy. Meanwhile, based on the diffusion model, preferred actions within the same behavior distribution are automatically generated through the critic function. Moreover, an anti-noise preference optimization is designed to achieve policy improvement by using the preferred actions, which can adapt to noise-preferred actions for stable training. Extensive experiments demonstrate that the proposed method provides competitive or superior performance compared to previous state-of-the-art offline RL methods, particularly in sparse reward tasks such as Kitchen and AntMaze. Additionally, we empirically prove the effectiveness of anti-noise preference optimization. | [
"['Tianle Zhang' 'Jiayi Guan' 'Lin Zhao' 'Yihang Li' 'Dongjiang Li'\n 'Zecui Zeng' 'Lei Sun' 'Yue Chen' 'Xuelong Wei' 'Lusong Li' 'Xiaodong He']"
] |
null | null | 2405.18732 | null | null | http://arxiv.org/pdf/2405.18732v3 | 2024-06-14T17:12:17Z | 2024-05-29T03:23:34Z | Gemini & Physical World: Large Language Models Can Estimate the
Intensity of Earthquake Shaking from Multi-Modal Social Media Posts | This paper presents a novel approach to extract scientifically valuable information about Earth's physical phenomena from unconventional sources, such as multi-modal social media posts. Employing a state-of-the-art large language model (LLM), Gemini 1.5 Pro (Reid et al. 2024), we estimate earthquake ground shaking intensity from these unstructured posts. The model's output, in the form of Modified Mercalli Intensity (MMI) values, aligns well with independent observational data. Furthermore, our results suggest that LLMs, trained on vast internet data, may have developed a unique understanding of physical phenomena. Specifically, Google's Gemini models demonstrate a simplified understanding of the general relationship between earthquake magnitude, distance, and MMI intensity, accurately describing observational data even though it's not identical to established models. These findings raise intriguing questions about the extent to which Gemini's training has led to a broader understanding of the physical world and its phenomena. The ability of Generative AI models like Gemini to generate results consistent with established scientific knowledge highlights their potential to augment our understanding of complex physical phenomena like earthquakes. The flexible and effective approach proposed in this study holds immense potential for enriching our understanding of the impact of physical phenomena and improving resilience during natural disasters. This research is a significant step toward harnessing the power of social media and AI for natural disaster mitigation, opening new avenues for understanding the emerging capabilities of Generative AI and LLMs for scientific applications. | [
"['S. Mostafa Mousavi' 'Marc Stogaitis' 'Tajinder Gadh' 'Richard M Allen'\n 'Alexei Barski' 'Robert Bosch' 'Patrick Robertson' 'Nivetha Thiruverahan'\n 'Youngmin Cho' 'Aman Raj']"
] |
null | null | 2405.18746 | null | null | http://arxiv.org/pdf/2405.18746v1 | 2024-05-29T04:09:46Z | 2024-05-29T04:09:46Z | STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks
from Untrusted Cloud | The high expenses imposed by current quantum cloud providers, coupled with the escalating need for quantum resources, may incentivize the emergence of cheaper cloud-based quantum services from potentially untrusted providers. Deploying or hosting quantum models, such as Quantum Neural Networks (QNNs), on these untrusted platforms introduces a myriad of security concerns, with the most critical one being model theft. This vulnerability stems from the cloud provider's full access to these circuits during training and/or inference. In this work, we introduce STIQ, a novel ensemble-based strategy designed to safeguard QNNs against such cloud-based adversaries. Our method innovatively trains two distinct QNNs concurrently, hosting them on same or different platforms, in a manner that each network yields obfuscated outputs rendering the individual QNNs ineffective for adversaries operating within cloud environments. However, when these outputs are combined locally (using an aggregate function), they reveal the correct result. Through extensive experiments across various QNNs and datasets, our technique has proven to effectively masks the accuracy and losses of the individually hosted models by upto 76%, albeit at the expense of $leq 2times$ increase in the total computational overhead. This trade-off, however, is a small price to pay for the enhanced security and integrity of QNNs in a cloud-based environment prone to untrusted adversaries. We also demonstrated STIQ's practical application by evaluating it on real 127-qubit IBM_Sherbrooke hardware, showing that STIQ achieves up to 60% obfuscation, with combined performance comparable to an unobfuscated model. | [
"['Satwik Kundu' 'Swaroop Ghosh']"
] |
null | null | 2405.18749 | null | null | http://arxiv.org/pdf/2405.18749v2 | 2024-06-03T00:17:05Z | 2024-05-29T04:22:18Z | A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody
Language Models | Antibodies are crucial proteins produced by the immune system to eliminate harmful foreign substances and have become pivotal therapeutic agents for treating human diseases. To accelerate the discovery of antibody therapeutics, there is growing interest in constructing language models using antibody sequences. However, the applicability of pre-trained language models for antibody discovery has not been thoroughly evaluated due to the scarcity of labeled datasets. To overcome these limitations, we introduce AVIDa-SARS-CoV-2, a dataset featuring the antigen-variable domain of heavy chain of heavy chain antibody (VHH) interactions obtained from two alpacas immunized with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins. AVIDa-SARS-CoV-2 includes binary labels indicating the binding or non-binding of diverse VHH sequences to 12 SARS-CoV-2 mutants, such as the Delta and Omicron variants. Furthermore, we release VHHCorpus-2M, a pre-training dataset for antibody language models, containing over two million VHH sequences. We report benchmark results for predicting SARS-CoV-2-VHH binding using VHHBERT pre-trained on VHHCorpus-2M and existing general protein and antibody-specific pre-trained language models. These results confirm that AVIDa-SARS-CoV-2 provides valuable benchmarks for evaluating the representation capabilities of antibody language models for binding prediction, thereby facilitating the development of AI-driven antibody discovery. The datasets are available at https://datasets.cognanous.com. | [
"['Hirofumi Tsuruta' 'Hiroyuki Yamazaki' 'Ryota Maeda' 'Ryotaro Tamura'\n 'Akihiro Imura']"
] |
null | null | 2405.18753 | null | null | http://arxiv.org/pdf/2405.18753v1 | 2024-05-29T04:37:19Z | 2024-05-29T04:37:19Z | Confronting the Reproducibility Crisis: A Case Study in Validating
Certified Robustness | Reproducibility is a cornerstone of scientific research, enabling validation, extension, and progress. However, the rapidly evolving nature of software and dependencies poses significant challenges to reproducing research results, particularly in fields like adversarial robustness for deep neural networks, where complex codebases and specialized toolkits are utilized. This paper presents a case study of attempting to validate the results on certified adversarial robustness in "SoK: Certified Robustness for Deep Neural Networks" using the VeriGauge toolkit. Despite following the documented methodology, numerous software and hardware compatibility issues were encountered, including outdated or unavailable dependencies, version conflicts, and driver incompatibilities. While a subset of the original results could be run, key findings related to the empirical robust accuracy of various verification methods proved elusive due to these technical obstacles, as well as slight discrepancies in the test results. This practical experience sheds light on the reproducibility crisis afflicting adversarial robustness research, where a lack of reproducibility threatens scientific integrity and hinders progress. The paper discusses the broader implications of this crisis, proposing potential solutions such as containerization, software preservation, and comprehensive documentation practices. Furthermore, it highlights the need for collaboration and standardization efforts within the research community to develop robust frameworks for reproducible research. By addressing the reproducibility crisis head-on, this work aims to contribute to the ongoing discourse on scientific reproducibility and advocate for best practices that ensure the reliability and validity of research findings within not only adversarial robustness, but security and technology research as a whole. | [
"['Richard H. Moulton' 'Gary A. McCully' 'John D. Hastings']"
] |
null | null | 2405.18754 | null | null | http://arxiv.org/pdf/2405.18754v1 | 2024-05-29T04:39:24Z | 2024-05-29T04:39:24Z | GIST: Greedy Independent Set Thresholding for Diverse Data Summarization | We propose a novel subset selection task called min-distance diverse data summarization ($textsf{MDDS}$), which has a wide variety of applications in machine learning, e.g., data sampling and feature selection. Given a set of points in a metric space, the goal is to maximize an objective that combines the total utility of the points and a diversity term that captures the minimum distance between any pair of selected points, subject to the constraint $|S| le k$. For example, the points may correspond to training examples in a data sampling problem, e.g., learned embeddings of images extracted from a deep neural network. This work presents the $texttt{GIST}$ algorithm, which achieves a $frac{2}{3}$-approximation guarantee for $textsf{MDDS}$ by approximating a series of maximum independent set problems with a bicriteria greedy algorithm. We also prove a complementary $(frac{2}{3}+varepsilon)$-hardness of approximation, for any $varepsilon > 0$. Finally, we provide an empirical study that demonstrates $texttt{GIST}$ outperforms existing methods for $textsf{MDDS}$ on synthetic data, and also for a real-world image classification experiment the studies single-shot subset selection for ImageNet. | [
"['Matthew Fahrbach' 'Srikumar Ramalingam' 'Morteza Zadimoghaddam'\n 'Sara Ahmadian' 'Gui Citovsky' 'Giulia DeSalvo']"
] |
null | null | 2405.18756 | null | null | http://arxiv.org/pdf/2405.18756v1 | 2024-05-29T04:48:11Z | 2024-05-29T04:48:11Z | Provable Contrastive Continual Learning | Continual learning requires learning incremental tasks with dynamic data distributions. So far, it has been observed that employing a combination of contrastive loss and distillation loss for training in continual learning yields strong performance. To the best of our knowledge, however, this contrastive continual learning framework lacks convincing theoretical explanations. In this work, we fill this gap by establishing theoretical performance guarantees, which reveal how the performance of the model is bounded by training losses of previous tasks in the contrastive continual learning framework. Our theoretical explanations further support the idea that pre-training can benefit continual learning. Inspired by our theoretical analysis of these guarantees, we propose a novel contrastive continual learning algorithm called CILA, which uses adaptive distillation coefficients for different tasks. These distillation coefficients are easily computed by the ratio between average distillation losses and average contrastive losses from previous tasks. Our method shows great improvement on standard benchmarks and achieves new state-of-the-art performance. | [
"['Yichen Wen' 'Zhiquan Tan' 'Kaipeng Zheng' 'Chuanlong Xie' 'Weiran Huang']"
] |
null | null | 2405.18758 | null | null | http://arxiv.org/pdf/2405.18758v1 | 2024-05-29T04:53:31Z | 2024-05-29T04:53:31Z | Learning to Continually Learn with the Bayesian Principle | In the present era of deep learning, continual learning research is mainly focused on mitigating forgetting when training a neural network with stochastic gradient descent on a non-stationary stream of data. On the other hand, in the more classical literature of statistical machine learning, many models have sequential Bayesian update rules that yield the same learning outcome as the batch training, i.e., they are completely immune to catastrophic forgetting. However, they are often overly simple to model complex real-world data. In this work, we adopt the meta-learning paradigm to combine the strong representational power of neural networks and simple statistical models' robustness to forgetting. In our novel meta-continual learning framework, continual learning takes place only in statistical models via ideal sequential Bayesian update rules, while neural networks are meta-learned to bridge the raw data and the statistical models. Since the neural networks remain fixed during continual learning, they are protected from catastrophic forgetting. This approach not only achieves significantly improved performance but also exhibits excellent scalability. Since our approach is domain-agnostic and model-agnostic, it can be applied to a wide range of problems and easily integrated with existing model architectures. | [
"['Soochan Lee' 'Hyeonseong Jeon' 'Jaehyeon Son' 'Gunhee Kim']"
] |
null | null | 2405.18761 | null | null | http://arxiv.org/pdf/2405.18761v1 | 2024-05-29T05:00:50Z | 2024-05-29T05:00:50Z | FDQN: A Flexible Deep Q-Network Framework for Game Automation | In reinforcement learning, it is often difficult to automate high-dimensional, rapid decision-making in dynamic environments, especially when domains require real-time online interaction and adaptive strategies such as web-based games. This work proposes a state-of-the-art Flexible Deep Q-Network (FDQN) framework that can address this challenge with a selfadaptive approach that is processing high-dimensional sensory data in realtime using a CNN and dynamically adapting the model architecture to varying action spaces of different gaming environments and outperforming previous baseline models in various Atari games and the Chrome Dino game as baselines. Using the epsilon-greedy policy, it effectively balances the new learning and exploitation for improved performance, and it has been designed with a modular structure that it can be easily adapted to other HTML-based games without touching the core part of the framework. It is demonstrated that the FDQN framework can successfully solve a well-defined task in a laboratory condition, but more importantly it also discusses potential applications to more challenging real-world cases and serve as the starting point for future further exploration into automated game play and beyond. | [
"['Prabhath Reddy Gujavarthy']"
] |
null | null | 2405.18765 | null | null | http://arxiv.org/pdf/2405.18765v1 | 2024-05-29T05:08:16Z | 2024-05-29T05:08:16Z | Large Brain Model for Learning Generic Representations with Tremendous
EEG Data in BCI | The current electroencephalogram (EEG) based deep learning models are typically designed for specific datasets and applications in brain-computer interaction (BCI), limiting the scale of the models and thus diminishing their perceptual capabilities and generalizability. Recently, Large Language Models (LLMs) have achieved unprecedented success in text processing, prompting us to explore the capabilities of Large EEG Models (LEMs). We hope that LEMs can break through the limitations of different task types of EEG datasets, and obtain universal perceptual capabilities of EEG signals through unsupervised pre-training. Then the models can be fine-tuned for different downstream tasks. However, compared to text data, the volume of EEG datasets is generally small and the format varies widely. For example, there can be mismatched numbers of electrodes, unequal length data samples, varied task designs, and low signal-to-noise ratio. To overcome these challenges, we propose a unified foundation model for EEG called Large Brain Model (LaBraM). LaBraM enables cross-dataset learning by segmenting the EEG signals into EEG channel patches. Vector-quantized neural spectrum prediction is used to train a semantically rich neural tokenizer that encodes continuous raw EEG channel patches into compact neural codes. We then pre-train neural Transformers by predicting the original neural codes for the masked EEG channel patches. The LaBraMs were pre-trained on about 2,500 hours of various types of EEG signals from around 20 datasets and validated on multiple different types of downstream tasks. Experiments on abnormal detection, event type classification, emotion recognition, and gait prediction show that our LaBraM outperforms all compared SOTA methods in their respective fields. Our code is available at https://github.com/935963004/LaBraM. | [
"['Wei-Bang Jiang' 'Li-Ming Zhao' 'Bao-Liang Lu']"
] |
null | null | 2405.18768 | null | null | http://arxiv.org/pdf/2405.18768v2 | 2024-06-09T16:13:02Z | 2024-05-29T05:10:25Z | RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow
Matching | The growing significance of RNA engineering in diverse biological applications has spurred interest in developing AI methods for structure-based RNA design. While diffusion models have excelled in protein design, adapting them for RNA presents new challenges due to RNA's conformational flexibility and the computational cost of fine-tuning large structure prediction models. To this end, we propose RNAFlow, a flow matching model for protein-conditioned RNA sequence-structure design. Its denoising network integrates an RNA inverse folding model and a pre-trained RosettaFold2NA network for generation of RNA sequences and structures. The integration of inverse folding in the structure denoising process allows us to simplify training by fixing the structure prediction network. We further enhance the inverse folding model by conditioning it on inferred conformational ensembles to model dynamic RNA conformations. Evaluation on protein-conditioned RNA structure and sequence generation tasks demonstrates RNAFlow's advantage over existing RNA design methods. | [
"['Divya Nori' 'Wengong Jin']"
] |
null | null | 2405.18776 | null | null | http://arxiv.org/pdf/2405.18776v1 | 2024-05-29T05:32:50Z | 2024-05-29T05:32:50Z | LMO-DP: Optimizing the Randomization Mechanism for Differentially
Private Fine-Tuning (Large) Language Models | Differentially Private Stochastic Gradient Descent (DP-SGD) and its variants have been proposed to ensure rigorous privacy for fine-tuning large-scale pre-trained language models. However, they rely heavily on the Gaussian mechanism, which may overly perturb the gradients and degrade the accuracy, especially in stronger privacy regimes (e.g., the privacy budget $epsilon < 3$). To address such limitations, we propose a novel Language Model-based Optimal Differential Privacy (LMO-DP) mechanism, which takes the first step to enable the tight composition of accurately fine-tuning (large) language models with a sub-optimal DP mechanism, even in strong privacy regimes (e.g., $0.1leq epsilon<3$). Furthermore, we propose a novel offline optimal noise search method to efficiently derive the sub-optimal DP that significantly reduces the noise magnitude. For instance, fine-tuning RoBERTa-large (with 300M parameters) on the SST-2 dataset can achieve an accuracy of 92.20% (given $epsilon=0.3$, $delta=10^{-10}$) by drastically outperforming the Gaussian mechanism (e.g., $sim 50%$ for small $epsilon$ and $delta$). We also draw similar findings on the text generation tasks on GPT-2. Finally, to our best knowledge, LMO-DP is also the first solution to accurately fine-tune Llama-2 with strong differential privacy guarantees. The code will be released soon and available upon request. | [
"['Qin Yang' 'Meisam Mohammad' 'Han Wang' 'Ali Payani' 'Ashish Kundu'\n 'Kai Shu' 'Yan Yan' 'Yuan Hong']"
] |
null | null | 2405.18777 | null | null | http://arxiv.org/pdf/2405.18777v1 | 2024-05-29T05:36:03Z | 2024-05-29T05:36:03Z | SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm
Achieving Optimal Sample Complexity | While stochastic bilevel optimization methods have been extensively studied for addressing large-scale nested optimization problems in machine learning, it remains an open question whether the optimal complexity bounds for solving bilevel optimization are the same as those in single-level optimization. Our main result resolves this question: SPABA, an adaptation of the PAGE method for nonconvex optimization in (Li et al., 2021) to the bilevel setting, can achieve optimal sample complexity in both the finite-sum and expectation settings. We show the optimality of SPABA by proving that there is no gap in complexity analysis between stochastic bilevel and single-level optimization when implementing PAGE. Notably, as indicated by the results of (Dagr'eou et al., 2022), there might exist a gap in complexity analysis when implementing other stochastic gradient estimators, like SGD and SAGA. In addition to SPABA, we propose several other single-loop stochastic bilevel algorithms, that either match or improve the state-of-the-art sample complexity results, leveraging our convergence rate and complexity analysis. Numerical experiments demonstrate the superior practical performance of the proposed methods. | [
"['Tianshu Chu' 'Dachuan Xu' 'Wei Yao' 'Jin Zhang']"
] |
null | null | 2405.18780 | null | null | http://arxiv.org/pdf/2405.18780v1 | 2024-05-29T05:39:37Z | 2024-05-29T05:39:37Z | Quantitative Certification of Bias in Large Language Models | Large Language Models (LLMs) can produce responses that exhibit social biases and support stereotypes. However, conventional benchmarking is insufficient to thoroughly evaluate LLM bias, as it can not scale to large sets of prompts and provides no guarantees. Therefore, we propose a novel certification framework QuaCer-B (Quantitative Certification of Bias) that provides formal guarantees on obtaining unbiased responses from target LLMs under large sets of prompts. A certificate consists of high-confidence bounds on the probability of obtaining biased responses from the LLM for any set of prompts containing sensitive attributes, sampled from a distribution. We illustrate the bias certification in LLMs for prompts with various prefixes drawn from given distributions. We consider distributions of random token sequences, mixtures of manual jailbreaks, and jailbreaks in the LLM's embedding space to certify its bias. We certify popular LLMs with QuaCer-B and present novel insights into their biases. | [
"['Isha Chaudhary' 'Qian Hu' 'Manoj Kumar' 'Morteza Ziyadi' 'Rahul Gupta'\n 'Gagandeep Singh']"
] |
null | null | 2405.18781 | null | null | http://arxiv.org/pdf/2405.18781v1 | 2024-05-29T05:41:28Z | 2024-05-29T05:41:28Z | On the Role of Attention Masks and LayerNorm in Transformers | Self-attention is the key mechanism of transformers, which are the essential building blocks of modern foundation models. Recent studies have shown that pure self-attention suffers from an increasing degree of rank collapse as depth increases, limiting model expressivity and further utilization of model depth. The existing literature on rank collapse, however, has mostly overlooked other critical components in transformers that may alleviate the rank collapse issue. In this paper, we provide a general analysis of rank collapse under self-attention, taking into account the effects of attention masks and layer normalization (LayerNorm). In particular, we find that although pure masked attention still suffers from exponential collapse to a rank one subspace, local masked attention can provably slow down the collapse rate. In the case of self-attention with LayerNorm, we first show that for certain classes of value matrices, collapse to a rank one subspace still happens exponentially. However, through construction of nontrivial counterexamples, we then establish that with proper choice of value matrices, a general class of sequences may not converge to a rank one subspace, and the self-attention dynamics with LayerNorm can simultaneously possess a rich set of equilibria with any possible rank between one and full. Our result refutes the previous hypothesis that LayerNorm plays no role in the rank collapse of self-attention and suggests that self-attention with LayerNorm constitutes a much more expressive, versatile nonlinear dynamical system than what was originally thought. | [
"['Xinyi Wu' 'Amir Ajorlou' 'Yifei Wang' 'Stefanie Jegelka' 'Ali Jadbabaie']"
] |
null | null | 2405.18786 | null | null | http://arxiv.org/pdf/2405.18786v1 | 2024-05-29T05:59:52Z | 2024-05-29T05:59:52Z | MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing
Optimized Kernel Dependence | In cross-domain few-shot classification, emph{nearest centroid classifier} (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, emph{maximizing optimized kernel dependence} (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in emph{Hilbert-Schmidt independence criterion} (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-Dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: href{https://github.com/tmlr-group/MOKD}{https://github.com/tmlr-group/MOKD}. | [
"['Hongduan Tian' 'Feng Liu' 'Tongliang Liu' 'Bo Du' 'Yiu-ming Cheung'\n 'Bo Han']"
] |
null | null | 2405.18792 | null | null | http://arxiv.org/pdf/2405.18792v1 | 2024-05-29T06:17:33Z | 2024-05-29T06:17:33Z | Kernel Metric Learning for In-Sample Off-Policy Evaluation of
Deterministic RL Policies | We consider off-policy evaluation (OPE) of deterministic target policies for reinforcement learning (RL) in environments with continuous action spaces. While it is common to use importance sampling for OPE, it suffers from high variance when the behavior policy deviates significantly from the target policy. In order to address this issue, some recent works on OPE proposed in-sample learning with importance resampling. Yet, these approaches are not applicable to deterministic target policies for continuous action spaces. To address this limitation, we propose to relax the deterministic target policy using a kernel and learn the kernel metrics that minimize the overall mean squared error of the estimated temporal difference update vector of an action value function, where the action value function is used for policy evaluation. We derive the bias and variance of the estimation error due to this relaxation and provide analytic solutions for the optimal kernel metric. In empirical studies using various test domains, we show that the OPE with in-sample learning using the kernel with optimized metric achieves significantly improved accuracy than other baselines. | [
"['Haanvid Lee' 'Tri Wahyu Guntara' 'Jongmin Lee' 'Yung-Kyun Noh'\n 'Kee-Eung Kim']"
] |
null | null | 2405.18793 | null | null | http://arxiv.org/pdf/2405.18793v1 | 2024-05-29T06:18:09Z | 2024-05-29T06:18:09Z | Adaptive Discretization-based Non-Episodic Reinforcement Learning in
Metric Spaces | We study non-episodic Reinforcement Learning for Lipschitz MDPs in which state-action space is a metric space, and the transition kernel and rewards are Lipschitz functions. We develop computationally efficient UCB-based algorithm, $textit{ZoRL-}epsilon$ that adaptively discretizes the state-action space and show that their regret as compared with $epsilon$-optimal policy is bounded as $mathcal{O}(epsilon^{-(2 d_mathcal{S} + d^epsilon_z + 1)}log{(T)})$, where $d^epsilon_z$ is the $epsilon$-zooming dimension. In contrast, if one uses the vanilla $textit{UCRL-}2$ on a fixed discretization of the MDP, the regret w.r.t. a $epsilon$-optimal policy scales as $mathcal{O}(epsilon^{-(2 d_mathcal{S} + d + 1)}log{(T)})$ so that the adaptivity gains are huge when $d^epsilon_z ll d$. Note that the absolute regret of any 'uniformly good' algorithm for a large family of continuous MDPs asymptotically scales as at least $Omega(log{(T)})$. Though adaptive discretization has been shown to yield $mathcal{tilde{O}}(H^{2.5}K^frac{d_z + 1}{d_z + 2})$ regret in episodic RL, an attempt to extend this to the non-episodic case by employing constant duration episodes whose duration increases with $T$, is futile since $d_z to d$ as $T to infty$. The current work shows how to obtain adaptivity gains for non-episodic RL. The theoretical results are supported by simulations on two systems where the performance of $textit{ZoRL-}epsilon$ is compared with that of '$textit{UCRL-C}$,' the fixed discretization-based extension of $textit{UCRL-}2$ for systems with continuous state-action spaces. | [
"['Avik Kar' 'Rahul Singh']"
] |
null | null | 2405.18795 | null | null | http://arxiv.org/pdf/2405.18795v1 | 2024-05-29T06:26:52Z | 2024-05-29T06:26:52Z | Federated Q-Learning with Reference-Advantage Decomposition: Almost
Optimal Regret and Logarithmic Communication Cost | In this paper, we consider model-free federated reinforcement learning for tabular episodic Markov decision processes. Under the coordination of a central server, multiple agents collaboratively explore the environment and learn an optimal policy without sharing their raw data. Despite recent advances in federated Q-learning algorithms achieving near-linear regret speedup with low communication cost, existing algorithms only attain suboptimal regrets compared to the information bound. We propose a novel model-free federated Q-learning algorithm, termed FedQ-Advantage. Our algorithm leverages reference-advantage decomposition for variance reduction and operates under two distinct mechanisms: synchronization between the agents and the server, and policy update, both triggered by events. We prove that our algorithm not only requires a lower logarithmic communication cost but also achieves an almost optimal regret, reaching the information bound up to a logarithmic factor and near-linear regret speedup compared to its single-agent counterpart when the time horizon is sufficiently large. | [
"['Zhong Zheng' 'Haochen Zhang' 'Lingzhou Xue']"
] |
null | null | 2405.18805 | null | null | http://arxiv.org/pdf/2405.18805v2 | 2024-07-05T07:45:16Z | 2024-05-29T06:47:45Z | Semiring Activation in Neural Networks | We introduce a class of trainable nonlinear operators based on semirings that are suitable for use in neural networks. These operators generalize the traditional alternation of linear operators with activation functions in neural networks. Semirings are algebraic structures that describe a generalised notation of linearity, greatly expanding the range of trainable operators that can be included in neural networks. In fact, max- or min-pooling operations are convolutions in the tropical semiring with a fixed kernel. We perform experiments where we replace the activation functions for trainable semiring-based operators to show that these are viable operations to include in fully connected as well as convolutional neural networks (ConvNeXt). We discuss some of the challenges of replacing traditional activation functions with trainable semiring activations and the trade-offs of doing so. | [
"['Bart M. N. Smets' 'Peter D. Donker' 'Jim W. Portegies' 'Remco Duits']"
] |
null | null | 2405.18816 | null | null | http://arxiv.org/pdf/2405.18816v1 | 2024-05-29T06:56:12Z | 2024-05-29T06:56:12Z | Flow Priors for Linear Inverse Problems via Iterative Corrupted
Trajectory Matching | Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis. By leveraging the instantaneous change-of-variables formula, one can directly compute image likelihoods from a learned flow, making them enticing candidates as priors for downstream tasks such as inverse problems. In particular, a natural approach would be to incorporate such image probabilities in a maximum-a-posteriori (MAP) estimation problem. A major obstacle, however, lies in the slow computation of the log-likelihood, as it requires backpropagating through an ODE solver, which can be prohibitively slow for high-dimensional problems. In this work, we propose an iterative algorithm to approximate the MAP estimator efficiently to solve a variety of linear inverse problems. Our algorithm is mathematically justified by the observation that the MAP objective can be approximated by a sum of $N$ ``local MAP'' objectives, where $N$ is the number of function evaluations. By leveraging Tweedie's formula, we show that we can perform gradient steps to sequentially optimize these objectives. We validate our approach for various linear inverse problems, such as super-resolution, deblurring, inpainting, and compressed sensing, and demonstrate that we can outperform other methods based on flow matching. | [
"['Yasi Zhang' 'Peiyu Yu' 'Yaxuan Zhu' 'Yingshan Chang' 'Feng Gao'\n 'Ying Nian Wu' 'Oscar Leong']"
] |
null | null | 2405.18831 | null | null | http://arxiv.org/pdf/2405.18831v1 | 2024-05-29T07:20:28Z | 2024-05-29T07:20:28Z | Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering
Benchmarks | As interest in "reformulating" the 3D Visual Question Answering (VQA) problem in the context of foundation models grows, it is imperative to assess how these new paradigms influence existing closed-vocabulary datasets. In this case study, we evaluate the zero-shot performance of foundational models (GPT-4 Vision and GPT-4) on well-established 3D VQA benchmarks, namely 3D-VQA and ScanQA. We provide an investigation to contextualize the performance of GPT-based agents relative to traditional modeling approaches. We find that GPT-based agents without any fine-tuning perform on par with the closed vocabulary approaches. Our findings corroborate recent results that "blind" models establish a surprisingly strong baseline in closed-vocabulary settings. We demonstrate that agents benefit significantly from scene-specific vocabulary via in-context textual grounding. By presenting a preliminary comparison with previous baselines, we hope to inform the community's ongoing efforts to refine multi-modal 3D benchmarks. | [
"['Simranjit Singh' 'Georgios Pavlakos' 'Dimitrios Stamoulis']"
] |
null | null | 2405.18832 | null | null | http://arxiv.org/pdf/2405.18832v1 | 2024-05-29T07:23:29Z | 2024-05-29T07:23:29Z | MoNDE: Mixture of Near-Data Experts for Large-Scale Sparse Models | Mixture-of-Experts (MoE) large language models (LLM) have memory requirements that often exceed the GPU memory capacity, requiring costly parameter movement from secondary memories to the GPU for expert computation. In this work, we present Mixture of Near-Data Experts (MoNDE), a near-data computing solution that efficiently enables MoE LLM inference. MoNDE reduces the volume of MoE parameter movement by transferring only the $textit{hot}$ experts to the GPU, while computing the remaining $textit{cold}$ experts inside the host memory device. By replacing the transfers of massive expert parameters with the ones of small activations, MoNDE enables far more communication-efficient MoE inference, thereby resulting in substantial speedups over the existing parameter offloading frameworks for both encoder and decoder operations. | [
"['Taehyun Kim' 'Kwanseok Choi' 'Youngmock Cho' 'Jaehoon Cho'\n 'Hyuk-Jae Lee' 'Jaewoong Sim']"
] |
null | null | 2405.18836 | null | null | http://arxiv.org/pdf/2405.18836v1 | 2024-05-29T07:31:18Z | 2024-05-29T07:31:18Z | Do Finetti: On Causal Effects for Exchangeable Data | We study causal effect estimation in a setting where the data are not i.i.d. (independent and identically distributed). We focus on exchangeable data satisfying an assumption of independent causal mechanisms. Traditional causal effect estimation frameworks, e.g., relying on structural causal models and do-calculus, are typically limited to i.i.d. data and do not extend to more general exchangeable generative processes, which naturally arise in multi-environment data. To address this gap, we develop a generalized framework for exchangeable data and introduce a truncated factorization formula that facilitates both the identification and estimation of causal effects in our setting. To illustrate potential applications, we introduce a causal P'olya urn model and demonstrate how intervention propagates effects in exchangeable data settings. Finally, we develop an algorithm that performs simultaneous causal discovery and effect estimation given multi-environment data. | [
"['Siyuan Guo' 'Chi Zhang' 'Karthika Mohan' 'Ferenc Huszár'\n 'Bernhard Schölkopf']"
] |
null | null | 2405.18843 | null | null | http://arxiv.org/pdf/2405.18843v1 | 2024-05-29T07:50:47Z | 2024-05-29T07:50:47Z | Data-driven Machinery Fault Detection: A Comprehensive Review | In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the massive surge in industrial big data and advancement in sensing and computational technologies, data-driven Machinery Fault Diagnosis (MFD) solutions based on machine/deep learning approaches have been used ubiquitously in manufacturing. Timely and accurately identifying faulty machine signals is vital in industrial applications for which many relevant solutions have been proposed and are reviewed in many articles. Despite the availability of numerous solutions and reviews on MFD, existing works often lack several aspects. Most of the available literature has limited applicability in a wide range of manufacturing settings due to their concentration on a particular type of equipment or method of analysis. Additionally, discussions regarding the challenges associated with implementing data-driven approaches, such as dealing with noisy data, selecting appropriate features, and adapting models to accommodate new or unforeseen faults, are often superficial or completely overlooked. Thus, this survey provides a comprehensive review of the articles using different types of machine learning approaches for the detection and diagnosis of various types of machinery faults, highlights their strengths and limitations, provides a review of the methods used for condition-based analyses, comprehensively discusses the available machinery fault datasets, introduces future researchers to the possible challenges they have to encounter while using these approaches for MFD and recommends the probable solutions to mitigate those problems. The future research prospects are also pointed out for a better understanding of the field. We believe this article will help researchers and contribute to the further development of the field. | [
"['Dhiraj Neupane' 'Mohamed Reda Bouadjenek' 'Richard Dazeley'\n 'Sunil Aryal']"
] |
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