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[] | Poster | [] | Contrastive learning has emerged as a popular paradigm of self-supervised learning that learns representations by encouraging representations of positive pairs to be similar while representations of negative pairs to be far apart. The spectral contrastive loss, in synergy with the notion of positive-pair graphs, offers valuable theoretical insights into the empirical successes of contrastive learning. In this paper, we propose incorporating an additive factor into the term of spectral contrastive loss involving negative pairs. This simple modification can be equivalently viewed as introducing a regularization term that enforces the mean of representations to be zero, which is referred to as *zero-mean regularization*. It intuitively relaxes the orthogonality of representations between negative pairs and implicitly alleviates the adverse effect of wrong connections in the positive-pair graph, leading to better performance and robustness. To clarify this, we thoroughly investigate the role of zero-mean regularized spectral contrastive loss in both unsupervised and supervised scenarios with respect to theoretical analysis and quantitative evaluation. These results highlight the potential of zero-mean regularized spectral contrastive learning to be a promising approach in various tasks. | [] | [] | Zero-Mean Regularized Spectral Contrastive Learning: Implicitly Mitigating Wrong Connections in Positive-Pair Graphs | [
"Xiong Zhou",
"Xianming Liu",
"Feilong Zhang",
"Gang Wu",
"Deming Zhai",
"Junjun Jiang",
"Xiangyang Ji"
] | 18,639 | https://openreview.net/forum?id=RZBy8oHTz4 |
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[] | Poster | [] | The conventional formulation of Markov decision processes (MDPs) assumes that the agent's decisions are promptly executed.However, in numerous realistic applications such as robotics or healthcare, actions are performed with a delay which value can even be stochastic.In this work, we introduce stochastic delayed execution MDPs, a new formalism addressing random delays without resorting to state augmentation. We show that given observed delay values, it is sufficient to perform a policy search in the class of Markov policies in order to reach optimal performance, thus extending the deterministic fixed delay case. Armed with this insight, we devise Delayed EfficientZero, a model-based algorithm that optimizes over the class of Markov policies. Delayed EfficientZero leverages the Monte-Carlo tree search of its non-delayed variant EfficientZero to accurately infer future states from the action queue. Thus, it handles delayed execution while preserving the sample efficiency of EfficientZero. Through empirical analysis, we demonstrate that our algorithm surpasses all benchmark methods in Atari games when dealing with both constant and stochastic delays. | [] | [] | Tree Search-Based Policy Optimization under Stochastic Execution Delay | [
"David Valensi",
"Esther Derman",
"Shie Mannor",
"Gal Dalal"
] | 2404.05440 | 18,638 | https://openreview.net/forum?id=RaqZX9LSGA |
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[] | Spotlight Poster | [] | Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is important to capture the diversity in human speech such as speaker identities, prosodies, and styles (e.g., singing). Current large TTS systems usually quantize speech into discrete tokens and use language models to generate these tokens one by one, which suffer from unstable prosody, word skipping/repeating issue, and poor voice quality. In this paper, we develop NaturalSpeech 2, a TTS system that leverages a neural audio codec with residual vector quantizers to get the quantized latent vectors and uses a diffusion model to generate these latent vectors conditioned on text input. To enhance the zero-shot capability that is important to achieve diverse speech synthesis, we design a speech prompting mechanism to facilitate in-context learning in the diffusion model and the duration/pitch predictor. We scale NaturalSpeech 2 to large-scale datasets with 44K hours of speech and singing data and evaluate its voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS systems by a large margin in terms of prosody/timbre similarity, robustness, and voice quality in a zero-shot setting, and performs novel zero-shot singing synthesis with only a speech prompt. Audio samples are available at https://naturalspeech2.github.io/. | [] | [] | NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers | [
"Kai Shen",
"Zeqian Ju",
"Xu Tan",
"Eric Liu",
"Yichong Leng",
"Lei He",
"Tao Qin",
"sheng zhao",
"Jiang Bian"
] | 2304.09116 | 18,637 | https://openreview.net/forum?id=Rc7dAwVL3v |
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[] | Poster | [] | While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity. Adding to the problem, direct transplantation of protein design methodologies into RNA design fails to achieve satisfactory outcomes although sharing similar structural components. In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. By constraining data representations within a limited hyperspherical space, the intrinsic relationships between data points could be explicitly imposed. Moreover, we incorporated extracted secondary structures with base pairs as prior knowledge to facilitate the RNA design process. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks. The source code and benchmark dataset will be released publicly. | [] | [] | RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design | [
"Cheng Tan",
"Yijie Zhang",
"Zhangyang Gao",
"Bozhen Hu",
"Siyuan Li",
"Zicheng Liu",
"Stan Z. Li"
] | 2301.10774 | 18,636 | https://openreview.net/forum?id=RemfXx7ebP |
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[] | Poster | [] | As artificial intelligence (AI) systems play an increasingly prominent role in human decision-making, challenges surface in the realm of human-AI interactions. One challenge arises from the suboptimal AI policies due to the inadequate consideration of humans disregarding AI recommendations, as well as the need for AI to provide advice selectively when it is most pertinent. This paper presents a sequential decision-making model that (i) takes into account the human's adherence level (the probability that the human follows/rejects machine advice) and (ii) incorporates a defer option so that the machine can temporarily refrain from making advice. We provide learning algorithms that learn the optimal advice policy and make advice only at critical time stamps. Compared to problem-agnostic reinforcement learning algorithms, our specialized learning algorithms not only enjoy better theoretical convergence properties but also show strong empirical performance. | [] | [] | Learning to Make Adherence-aware Advice | [
"Guanting Chen",
"Xiaocheng Li",
"Chunlin Sun",
"Hanzhao Wang"
] | 2310.00817 | 18,635 | https://openreview.net/forum?id=RgELE1dQXx |
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[] | Poster | [] | The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose a novel method for automatically generating high-quality content-grounded data. It consists of three stages: (a) Content Preparation, (b) Generation: creating task-specific examples from the content (e.g., question-answer pairs or summaries). (c) Filtering mechanism aiming to ensure the quality and faithfulness of the generated data. We showcase this methodology by generating large-scale data for synthetic Long-form question-answering (LFQA) and summarization. In a human evaluation, our generated data was found to be natural and of high quality. Furthermore, we compare models trained on our data with models trained on human-written data – ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization. We show that our models are on par with or outperforming models trained on human-generated data and consistently outperforming them in faithfulness. Finally, we applied our method to create LFQA data within the medical domain and compared a model trained on it with models trained on other domains. | [] | [] | Achieving Human Parity in Content-Grounded Datasets Generation | [
"Asaf Yehudai",
"Boaz Carmeli",
"Yosi Mass",
"Ofir Arviv",
"Nathaniel Mills",
"Eyal Shnarch",
"Leshem Choshen"
] | 2401.14367 | 18,634 | https://openreview.net/forum?id=RjYKTQ0L0W |
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[] | Poster | [] | Yang et al. (2023) recently showed how to use first-order gradient methods to solve general variational inequalities (VIs) under a limiting assumption that analytic solutions of specific subproblems are available. In this paper, we circumvent this assumption via a warm-starting technique where we solve subproblems approximately and initialize variables with the approximate solution found at the previous iteration. We prove the convergence of this method and show that the gap function of the last iterate of the method decreases at a rate of $\mathcal{O}(\frac{1}{\sqrt{K}})$ when the operator is $L$-Lipschitz and monotone. In numerical experiments, we show that this technique can converge much faster than its exact counterpart. Furthermore, for the cases when the inequality constraints are simple, we introduce an alternative variant of ACVI and establish its convergence under the same conditions.Finally, we relax the smoothness assumptions in Yang et al., yielding, to our knowledge, the first convergence result for VIs with general constraints that does not rely on the assumption that the operator is $L$-Lipschitz. | [] | [] | A Primal-Dual Approach to Solving Variational Inequalities with General Constraints | [
"Tatjana Chavdarova",
"Tong Yang",
"Matteo Pagliardini",
"Michael Jordan"
] | 18,631 | https://openreview.net/forum?id=RsztjXcvUf |
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[] | Poster | [] | A simple design recipe for deep Transformers is to compose identical building blocks. But standard transformer blocks are far from simple, interweaving attention and MLP sub-blocks with skip connections \& normalisation layers in precise arrangements. This complexity leads to brittle architectures, where seemingly minor changes can significantly reduce training speed, or render models untrainable.In this work, we ask to what extent the standard transformer block can be simplified? Combining signal propagation theory and empirical observations, we motivate modifications that allow many block components to be removed with no loss of training speed, including skip connections, projection or value parameters, sequential sub-blocks and normalisation layers. In experiments on both autoregressive decoder-only and BERT encoder-only models, our simplified transformers match the per-iteration training speed and performance of standard transformers, while enjoying 15\% faster training throughput, and using 15\% fewer parameters. | [] | [] | Simplifying Transformer Blocks | [
"Bobby He",
"Thomas Hofmann"
] | 2311.01906 | 18,629 | https://openreview.net/forum?id=RtDok9eS3s |
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[] | Poster | [] | We investigate the problem of transferring an expert policy from a source robot to multiple different robots. To solve this problem, we propose a method named *Meta-Evolve* that uses continuous robot evolution to efficiently transfer the policy to each target robot through a set of tree-structured evolutionary robot sequences. The robot evolution tree allows the robot evolution paths to be shared, so our approach can significantly outperform naive one-to-one policy transfer. We present a heuristic approach to determine an optimized robot evolution tree. Experiments have shown that our method is able to improve the efficiency of one-to-three transfer of manipulation policy by up to 3.2$\times$ and one-to-six transfer of agile locomotion policy by 2.4$\times$ in terms of simulation cost over the baseline of launching multiple independent one-to-one policy transfers. Supplementary videos available at the project website: https://sites.google.com/view/meta-evolve. | [] | [] | Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer | [
"Xingyu Liu",
"Deepak Pathak",
"Ding Zhao"
] | 18,628 | https://openreview.net/forum?id=RthOl4jHw5 |
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[] | Spotlight Poster | [] | Semi-supervised learning (SSL) has emerged as a promising paradigm to alleviate the dependency on abundant labeled data by harnessing the power of unlabeled data. Although many SSL algorithms have been proposed, their performance in practical applications is not robust because the assumption that labeled and unlabeled data are consistent does not hold. In open environments, the sources of labeled and unlabeled data may differ, leading to inconsistent data distributions and even data spaces. This paper points out that previous research on robust SSL has approached the problem from a static perspective, thereby only achieving local robustness rather than global robustness. We reshape the research framework of robust SSL by using the Robustness Analysis Curve (RAC) and the associated metrics defined based on it. Based on these metrics, we build a benchmark that encompasses three types of open environments: inconsistent data distributions, inconsistent label spaces, and inconsistent feature spaces to assess the performance of widely used statistical and deep SSL algorithms with tabular, image, and text datasets. This paper also conducted a detailed analysis, based on experimental results and theory, on how to make SSL algorithms more robust in open environments. | [] | [] | Realistic Evaluation of Semi-supervised Learning Algorithms in Open Environments | [
"Lin-Han Jia",
"Lan-Zhe Guo",
"Zhi Zhou",
"Yu-Feng Li"
] | 18,627 | https://openreview.net/forum?id=RvUVMjfp8i |
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[] | Spotlight Poster | [] | Large text corpora are the backbone of language models.However, we have a limited understanding of the content of these corpora, including general statistics, quality, social factors, and inclusion of evaluation data (contamination).In this work, we propose What's In My Big Data? (WIMBD), a platform and a set of 16 high-level analyses that allow us to reveal and compare the contents of large text corpora. WIMBD builds on two basic capabilities---count and search---*at scale*, which allows us to analyze more than 35 terabytes on a standard compute node. We apply WIMBD to 10 different corpora used to train popular language models, including *C4*, *The Pile*, and *RedPajama*.Our analysis uncovers several surprising and previously undocumented findings about these corpora, including the high prevalence of duplicate, synthetic, and low-quality content, personally identifiable information, toxic language, and benchmark contamination. For instance, we find that about 50% of the documents in *RedPajama* and *LAION-2B-en* are duplicates. In addition, several datasets used for benchmarking models trained on such corpora are contaminated with respect to important benchmarks, including the Winograd Schema Challenge and parts of GLUE and SuperGLUE.We open-source WIMBD code and artifacts to provide a standard set of evaluations for new text-based corpora and to encourage more analyses and transparency around them. | [] | [] | What's In My Big Data? | [
"Yanai Elazar",
"Akshita Bhagia",
"Ian Helgi Magnusson",
"Abhilasha Ravichander",
"Dustin Schwenk",
"Alane Suhr",
"Evan Pete Walsh",
"Dirk Groeneveld",
"Luca Soldaini",
"Sameer Singh",
"Hannaneh Hajishirzi",
"Noah A. Smith",
"Jesse Dodge"
] | 2310.20707 | 18,626 | https://openreview.net/forum?id=RvfPnOkPV4 |
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[] | Poster | [] | We introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the accurate prediction of clinical variables like age, anxiety, and PTSD as well as forecasting of future brain states. Critically, the model generalizes well to entirely new external cohorts not seen during training. In zero-shot inference mode, BrainLM can identify intrinsic functional networks directly from raw fMRI data without any network-based supervision during training. The model also generates interpretable latent representations that reveal relationships between brain activity patterns and cognitive states. Overall, BrainLM offers a versatile and interpretable framework for elucidating the complex spatiotemporal dynamics of human brain activity. It serves as a powerful "lens" through which massive repositories of fMRI data can be analyzed in new ways, enabling more effective interpretation and utilization at scale. The work demonstrates the potential of foundation models to advance computational neuroscience research. | [] | [] | BrainLM: A foundation model for brain activity recordings | [
"Josue Ortega Caro",
"Antonio Henrique de Oliveira Fonseca",
"Syed A Rizvi",
"Matteo Rosati",
"Christopher Averill",
"James L Cross",
"Prateek Mittal",
"Emanuele Zappala",
"Rahul Madhav Dhodapkar",
"Chadi Abdallah",
"David van Dijk"
] | 18,625 | https://openreview.net/forum?id=RwI7ZEfR27 |
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[] | Poster | [] | Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one, and has theoretical guarantees in terms of convergence. The proposed algorithm, that we call incremental predictive coding (iPC) is also more biologically plausible than the original one, as it it fully automatic. In an extensive set of experiments, we show that iPC constantly performs better than the original formulation on a large number of benchmarks for image classification, as well as for the training of both conditional and masked language models, in terms of test accuracy, efficiency, and convergence with respect to a large set of hyperparameters. | [] | [] | A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks | [
"Tommaso Salvatori",
"Yuhang Song",
"Yordan Yordanov",
"Beren Millidge",
"Lei Sha",
"Cornelius Emde",
"Zhenghua Xu",
"Rafal Bogacz",
"Thomas Lukasiewicz"
] | 2212.00720 | 18,624 | https://openreview.net/forum?id=RyUvzda8GH |
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[] | Poster | [] | The variational autoencoder (VAE) typically employs a standard normal prior as a regularizer for the probabilistic latent encoder. However, the Gaussian tail often decays too quickly to effectively accommodate the encoded points, failing to preserve crucial structures hidden in the data. In this paper, we explore the use of heavy-tailed models to combat over-regularization. Drawing upon insights from information geometry, we propose $t^3$VAE, a modified VAE framework that incorporates Student's t-distributions for the prior, encoder, and decoder. This results in a joint model distribution of a power form which we argue can better fit real-world datasets. We derive a new objective by reformulating the evidence lower bound as joint optimization of a KL divergence between two statistical manifolds and replacing with $\gamma$-power divergence, a natural alternative for power families. $t^3$VAE demonstrates superior generation of low-density regions when trained on heavy-tailed synthetic data. Furthermore, we show that our model excels at capturing rare features through real-data experiments on CelebA and imbalanced CIFAR datasets. | [] | [] | $t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence | [
"Juno Kim",
"Jaehyuk Kwon",
"Mincheol Cho",
"Hyunjong Lee",
"Joong-Ho Won"
] | 18,623 | https://openreview.net/forum?id=RzNlECeoOB |
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[] | Spotlight Poster | [] | Instruction-based image editing improves the controllability and flexibility of image manipulation via natural commands without elaborate descriptions or regional masks. However, human instructions are sometimes too brief for current methods to capture and follow. Multimodal large language models (MLLMs) show promising capabilities in cross-modal understanding and visual-aware response generation via LMs. We investigate how MLLMs facilitate edit instructions and present MLLM-Guided Image Editing (MGIE). MGIE learns to derive expressive instructions and provides explicit guidance. The editing model jointly captures this visual imagination and performs manipulation through end-to-end training. We evaluate various aspects of Photoshop-style modification, global photo optimization, and local editing. Extensive experimental results demonstrate that expressive instructions are crucial to instruction-based image editing, and our MGIE can lead to a notable improvement in automatic metrics and human evaluation while maintaining competitive inference efficiency. | [] | [] | Guiding Instruction-based Image Editing via Multimodal Large Language Models | [
"Tsu-Jui Fu",
"Wenze Hu",
"Xianzhi Du",
"William Yang Wang",
"Yinfei Yang",
"Zhe Gan"
] | 2309.17102 | 18,621 | https://openreview.net/forum?id=S1RKWSyZ2Y |
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[] | Poster | [] | Estimating treatment effects has numerous real-world applications in various fields, such as epidemiology and political science. While much attention has been devoted to addressing the challenge using fully observational data, there has been comparatively limited exploration of this issue in cases when the treatment is not directly observed. In this paper, we tackle this problem by developing a general variational framework, which is flexible to integrate with advanced neural network-based approaches, to identify the average dose-response function (ADRF) with the continuously valued error-contaminated treatment. Our approach begins with the formulation of a probabilistic data generation model, treating the unobserved treatment as a latent variable. In this model, we leverage a learnable density estimation neural network to derive its prior distribution conditioned on covariates. This module also doubles as a generalized propensity score estimator, effectively mitigating selection bias arising from observed confounding variables. Subsequently, we calculate the posterior distribution of the treatment, taking into account the observed measurement and outcome. To mitigate the impact of treatment error, we introduce a re-parametrized treatment value, replacing the error-affected one, to make more accurate predictions regarding the outcome. To demonstrate the adaptability of our framework, we incorporate two state-of-the-art ADRF estimation methods and rigorously assess its efficacy through extensive simulations and experiments using semi-synthetic data. | [] | [] | A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error | [
"Erdun Gao",
"Howard Bondell",
"Wei Huang",
"Mingming Gong"
] | 18,619 | https://openreview.net/forum?id=S46Knicu56 |
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[] | Poster | [
"https://github.com/PKU-ML/adainf"
] | Contrastive Learning (CL) has emerged as one of the most successful paradigms for unsupervised visual representation learning, yet it often depends on intensive manual data augmentations. With the rise of generative models, especially diffusion models, the ability to generate realistic images close to the real data distribution has been well recognized. These generated high-equality images have been successfully applied to enhance contrastive representation learning, a technique termed ``data inflation''. However, we find that the generated data (even from a good diffusion model like DDPM) may sometimes even harm contrastive learning. We investigate the causes behind this failure from the perspective of both data inflation and data augmentation. For the first time, we reveal the complementary roles that stronger data inflation should be accompanied by weaker augmentations, and vice versa. We also provide rigorous theoretical explanations for these phenomena via deriving its generalization bounds under data inflation. Drawing from these insights, we propose **Adaptive Inflation (AdaInf)**, a purely data-centric strategy without introducing any extra computation cost. On benchmark datasets, AdaInf can bring significant improvements for various contrastive learning methods. Notably, without using external data, AdaInf obtains 94.70% linear accuracy on CIFAR-10 with SimCLR, setting a new record that surpasses many sophisticated methods. | [] | [] | Do Generated Data Always Help Contrastive Learning? | [
"Yifei Wang",
"Jizhe Zhang",
"Yisen Wang"
] | 2403.12448 | 18,618 | https://openreview.net/forum?id=S5EqslEHnz |
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[] | Spotlight Poster | [] | Despite many successful examples in which probabilistic inference can account for perception, we have little understanding of how the brain represents and uses structured priors that capture the complexity of natural input statistics. Here we construct a recurrent circuit model that can implicitly represent priors over latent variables, and combine them with sensory and contextual sources of information to encode task-specific posteriors. Inspired by the recent success of diffusion models as means of learning and using priors over images, our model uses dendritic nonlinearities optimized for denoising, and stochastic somatic integration with the degree of noise modulated by an oscillating global signal. Combining these elements into a recurrent network yields a dynamical system that samples from the prior at a rate prescribed by the period of the global oscillator. Additional inputs reflecting sensory or top-down contextual information alter these dynamics to generate samples from the corresponding posterior, with different input gating patterns selecting different inference tasks. We demonstrate that this architecture can sample from low dimensional nonlinear manifolds and multimodal posteriors. Overall, the model provides a new framework for circuit-level representation of probabilistic information, in a format that facilitates flexible inference. | [] | [] | Complex priors and flexible inference in recurrent circuits with dendritic nonlinearities | [
"Benjamin S. H. Lyo",
"Cristina Savin"
] | 18,617 | https://openreview.net/forum?id=S5aUhpuyap |
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[] | Poster | [] | Multi-modal learning has become increasingly popular due to its ability to leverage information from different data sources (e.g., text and images) to improve the model performance. Recently, CLIP has emerged as an effective approach that employs vision-language contrastive pretraining to learn joint image and text representations and exhibits remarkable performance in zero-shot learning and text-guided natural image generation. Despite the substantial practical success of CLIP, its theoretical understanding remains elusive. In this paper, we formally study transferrable representation learning underlying CLIP and demonstrate how features from different modalities get aligned. We also analyze its zero-shot transfer performance on the downstream tasks. In addition, we conduct empirical evaluations on real data to backup our theory. Inspired by our analysis, we propose a new CLIP-type approach, which achieves better performance than CLIP and other state-of-the-art methods on benchmark datasets. | [] | [] | Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP | [
"Zixiang Chen",
"Yihe Deng",
"Yuanzhi Li",
"Quanquan Gu"
] | 2310.00927 | 18,616 | https://openreview.net/forum?id=S5yOuNfSA0 |
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[] | Poster | [] | This work presents an information-theoretic perspective to group fairness trade-offs in federated learning (FL) with respect to sensitive attributes, such as gender, race, etc. Existing works often focus on either \emph{global fairness} (overall disparity of the model across all clients) or \emph{local fairness} (disparity of the model at each client), without always considering their trade-offs. There is a lack of understanding of the interplay between global and local fairness in FL, particularly under data heterogeneity, and if and when one implies the other. To address this gap, we leverage a body of work in information theory called partial information decomposition (PID), which first identifies three sources of unfairness in FL, namely, \emph{Unique Disparity}, \emph{Redundant Disparity}, and \emph{Masked Disparity}. We demonstrate how these three disparities contribute to global and local fairness using canonical examples. This decomposition helps us derive fundamental limits on the trade-off between global and local fairness, highlighting where they agree or disagree. We introduce the \emph{Accuracy \& Global-Local Fairness Optimality Problem (AGLFOP)}, a convex optimization that defines the theoretical limits of accuracy and fairness trade-offs, identifying the best possible performance any FL strategy can attain given a dataset and client distribution. We also present experimental results on synthetic datasets and the ADULT dataset to support our theoretical findings. | [] | [] | Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition | [
"Faisal Hamman",
"Sanghamitra Dutta"
] | 2307.11333 | 18,614 | https://openreview.net/forum?id=SBj2Qdhgew |
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[] | Spotlight Poster | [] | Machine learning models are often used to decide who receives a loan, a job interview, or a public benefit. Standard methods to learn such models use features about people but overlook their actionability. As a result, models can assign predictions that are fixed – meaning that consumers who are denied loans, interviews, or benefits are precluded from access to credit, employment, or assistance. In this work, we present a task called recourse verification to flag models that assign fixed predictions under a rich class of real-world actionability constraints. We develop methods to check if a model can provide recourse using reachable sets. We demonstrate how our tools can verify recourse in real-world lending datasets. Our results highlight how models can inadvertently assign fixed predictions that permanently bar access, and underscore the need to account for actionability in model development. | [] | [] | Prediction without Preclusion: Recourse Verification with Reachable Sets | [
"Avni Kothari",
"Bogdan Kulynych",
"Tsui-Wei Weng",
"Berk Ustun"
] | 2308.12820 | 18,612 | https://openreview.net/forum?id=SCQfYpdoGE |
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[] | Spotlight Poster | [] | Most Neural Radiance Fields (NeRFs) exhibit limited generalization capabilities,which restrict their applicability in representing multiple scenes using a single model. To address this problem, existing generalizable NeRF methods simply condition the model on image features. These methods still struggle to learn precise global representations over diverse scenes since they lack an effective mechanism for interacting among different points and views. In this work, we unveil that3D implicit representation learning can be significantly improved by mask-based modeling. Specifically, we propose **m**asked **r**ay and **v**iew **m**odeling for generalizable **NeRF** (**MRVM-NeRF**), which is a self-supervised pretraining target to predict complete scene representations from partially masked features along each ray. With this pretraining target, MRVM-NeRF enables better use of correlations across different rays and views as the geometry priors, which thereby strengthens the capability of capturing intricate details within the scenes and boosts the generalization capability across different scenes. Extensive experiments demonstrate the effectiveness of our proposed MRVM-NeRF on both synthetic and real-world datasets, qualitatively and quantitatively. Besides, we also conduct experiments to show the compatibility of our proposed method with various backbones and itssuperiority under few-shot cases. | [] | [] | Mask-Based Modeling for Neural Radiance Fields | [
"Ganlin Yang",
"Guoqiang Wei",
"Zhizheng Zhang",
"Yan Lu",
"Dong Liu"
] | 2304.04962 | 18,611 | https://openreview.net/forum?id=SEiuSzlD1d |
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[] | Poster | [] | Machine unlearning aims to remove information derived from forgotten data while preserving that of the remaining dataset in a well-trained model. With the increasing emphasis on data privacy, several approaches to machine unlearning have emerged. However, these methods typically rely on complete supervision throughout the unlearning process. Unfortunately, obtaining such supervision, whether for the forgetting or remaining data, can be impractical due to the substantial cost associated with annotating real-world datasets. This challenge prompts us to propose a supervision-free unlearning approach that operates without the need for labels during the unlearning process. Specifically, we introduce a variational approach to approximate the distribution of representations for the remaining data. Leveraging this approximation, we adapt the original model to eliminate information from the forgotten data at the representation level. To further address the issue of lacking supervision information, which hinders alignment with ground truth, we introduce a contrastive loss to facilitate the matching of representations between the remaining data and those of the original model, thus preserving predictive performance. Experimental results across various unlearning tasks demonstrate the effectiveness of our proposed method, Label-Agnostic Forgetting (LAF) without using any labels, which achieves comparable performance to state-of-the-art methods that rely on full supervision information. Furthermore, our approach excels in semi-supervised scenarios, leveraging limited supervision information to outperform fully supervised baselines. This work not only showcases the viability of supervision-free unlearning in deep models but also opens up a new possibility for future research in unlearning at the representation level. | [] | [] | Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models | [
"Shaofei Shen",
"Chenhao Zhang",
"Yawen Zhao",
"Alina Bialkowski",
"Weitong Tony Chen",
"Miao Xu"
] | 2404.00506 | 18,610 | https://openreview.net/forum?id=SIZWiya7FE |
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[] | Poster | [] | Fair machine learning aims to prevent discrimination against individuals or sub-populations based on sensitive attributes such as gender and race. In recent years, causal inference methods have been increasingly used in fair machine learning to measure unfairness by causal effects. However, current methods assume that the true causal graph is given, which is often not true in real-world applications. To address this limitation, this paper proposes a framework for achieving causal fairness based on the notion of interventions when the true causal graph is partially known. The proposed approach involves modeling fair prediction using a Partially Directed Acyclic Graph (PDAG), specifically, a class of causal DAGs that can be learned from observational data combined with domain knowledge. The PDAG is used to measure causal fairness, and a constrained optimization problem is formulated to balance between fairness and accuracy. Results on both simulated and real-world datasets demonstrate the effectiveness of this method. | [] | [] | Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach | [
"Aoqi Zuo",
"Yiqing Li",
"Susan Wei",
"Mingming Gong"
] | 2401.10632 | 18,609 | https://openreview.net/forum?id=SKulT2VX9p |
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[] | Poster | [] | With the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era, Variational Quantum Algorithms (VQAs) have emerged as popular approaches to obtain possible quantum advantage in the relatively near future. In particular, how to effectively incorporate the common symmetries in physical systems as hard constraints in VQAs remains a critical and open question. In this paper, we revisit the Hamming Weight (HW) preserving ansatz and establish the links from ansatz to various symmetries and constraints, which both enlarges the usage of HW preserving ansatz and provides a coherent solution for constrained VQAs. Meanwhile, we utilize the quantum optimal control theory and quantum overparameterization theory to analyze the capability and expressivity of HW preserving ansatz and verify these theoretical results on unitary approximation problem. We further conduct detailed numerical experiments on two well-studied symmetry-preserving problems, namely ground state energy estimation and feature selection in machine learning. The superior performance demonstrates the efficiency and supremacy of the proposed HW preserving ansatz on constrained VQAs. | [] | [] | Rethinking the symmetry-preserving circuits for constrained variational quantum algorithms | [
"Ge Yan",
"Hongxu Chen",
"Kaisen Pan",
"Junchi Yan"
] | 18,608 | https://openreview.net/forum?id=SL7djdVpde |
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[] | Spotlight Poster | [
"https://github.com/eth-sri/language-model-arithmetic"
] | As Large Language Models (LLMs) are deployed more widely, customization with respect to vocabulary, style and character becomes more important. In this work we introduce model arithmetic, a novel inference framework for composing and biasing LLMs without the need for model (re)training or highly specific datasets. In addition, the framework allows for more precise control of generated text than direct prompting and prior controlled text generation (CTG) techniques. Using model arithmetic, we can express prior CTG techniques as simple formulas and naturally extend them to new and more effective formulations. Further, we show that speculative sampling, a technique for efficient LLM sampling, extends to our setting. This enables highly efficient text generation with multiple composed models with only marginal overhead over a single model. Our empirical evaluation demonstrates that model arithmetic allows fine-grained control of generated text while outperforming state-of-the-art on the task of toxicity reduction. We release an open source easy-to-use implementation of our framework at [ANONYMIZED]. | [] | [] | Controlled Text Generation via Language Model Arithmetic | [
"Jasper Dekoninck",
"Marc Fischer",
"Luca Beurer-Kellner",
"Martin Vechev"
] | 2311.14479 | 18,607 | https://openreview.net/forum?id=SLw9fp4yI6 |
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[] | Poster | [] | Pre-trained Language models (PLMs) have been acknowledged to contain harmful information, such as social biases, which may cause negative social impacts or even bring catastrophic results in application. Previous works on this problem mainly focused on using black-box methods such as probing to detect and quantify social biases in PLMs by observing model outputs. As a result, previous debiasing methods mainly finetune or even pre-train PLMs on newly constructed anti-stereotypical datasets, which are high-cost. In this work, we try to unveil the mystery of social bias inside language models by introducing the concept of {\sc Social Bias Neurons}. Specifically, we propose {\sc Integrated Gap Gradients (IG$^2$)} to accurately pinpoint units (i.e., neurons) in a language model that can be attributed to undesirable behavior, such as social bias. By formalizing undesirable behavior as a distributional property of language, we employ sentiment-bearing prompts to elicit classes of sensitive words (demographics) correlated with such sentiments. Our IG$^2$ thus attributes the uneven distribution for different demographics to specific Social Bias Neurons, which track the trail of unwanted behavior inside PLM units to achieve interoperability. Moreover, derived from our interpretable technique, {\sc Bias Neuron Suppression (BNS)} is further proposed to mitigate social biases. By studying BERT, RoBERTa, and their attributable differences from debiased FairBERTa, IG$^2$ allows us to locate and suppress identified neurons, and further mitigate undesired behaviors. As measured by prior metrics from StereoSet, our model achieves a higher degree of fairness while maintaining language modeling ability with low cost\footnote{This work contains examples that potentially implicate stereotypes, associations, and other harms that could be offensive to individuals in certain social groups.}. | [] | [] | The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Language Models | [
"Yan Liu",
"Yu Liu",
"Xiaokang Chen",
"Pin-Yu Chen",
"Daoguang Zan",
"Min-Yen Kan",
"Tsung-Yi Ho"
] | 18,605 | https://openreview.net/forum?id=SQGUDc9tC8 |
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[] | Poster | [] | Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating the space. To address this, we introduce \textit{Social-Transmotion}, a generic model that exploits the power of transformers to handle diverse and numerous visual cues, capturing the multi-modal nature of human behavior. We translate the idea of a prompt from Natural Language Processing (NLP) to the task of human trajectory prediction, where a prompt can be a sequence of x-y coordinates on the ground, bounding boxes or body poses. This, in turn, augments trajectory data, leading to enhanced human trajectory prediction.Our model exhibits flexibility and adaptability by capturing spatiotemporal interactions between pedestrians based on the available visual cues, whether they are poses, bounding boxes, or a combination thereof.By the masking technique, we ensure our model's effectiveness even when certain visual cues are unavailable, although performance is further boosted with the presence of comprehensive visual data.We delve into the merits of using 2d versus 3d poses, and a limited set of poses. Additionally, we investigate the spatial and temporal attention map to identify which keypoints and frames of poses are vital for optimizing human trajectory prediction.Our approach is validated on multiple datasets, including JTA, JRDB, Pedestrians and Cyclists in Road Traffic, and ETH-UCY. | [] | [] | Social-Transmotion: Promptable Human Trajectory Prediction | [
"Saeed Saadatnejad",
"Yang Gao",
"Kaouther Messaoud",
"Alexandre Alahi"
] | 18,604 | https://openreview.net/forum?id=SQpnEfv9WH |
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[] | Spotlight Poster | [] | Table understanding capability of Large Language Models (LLMs) has been extensively studied through the task of question-answering (QA) over tables. Typically, only a small part of the whole table is relevant to derive the answer for a given question. The irrelevant parts act as noise and are distracting information, resulting in sub-optimal performance due to the vulnerability of LLMs to noise. To mitigate this, we propose CABINET (Content RelevAnce-Based NoIse ReductioN for TablE QuesTion-Answering) – a framework to enable LLMs to focus on relevant tabular data by suppressing extraneous information. CABINET comprises an Unsupervised Relevance Scorer (URS), trained differentially with the QA LLM, that weighs the table content based on its relevance to the input question before feeding it to the question answering LLM (QA LLM). To further aid the relevance scorer, CABINET employs a weakly supervised module that generates a parsing statement describing the criteria of rows and columns relevant to the question and highlights the content of corresponding table cells. CABINET significantly outperforms various tabular LLM baselines, as well as GPT3-based in-context learning methods, is more robust to noise, maintains outperformance on tables of varying sizes, and establishes new SoTA performance on WikiTQ, FeTaQA, and WikiSQL datasets. We release our code and datasets here. | [] | [] | CABINET: Content Relevance-based Noise Reduction for Table Question Answering | [
"Sohan Patnaik",
"Heril Changwal",
"Milan Aggarwal",
"Sumit Bhatia",
"Yaman Kumar",
"Balaji Krishnamurthy"
] | 2402.01155 | 18,603 | https://openreview.net/forum?id=SQrHpTllXa |
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[] | Poster | [] | Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex topological and feature information of graphs. Graph transformer conventionally performs dense attention (or global attention) for every pair of nodes to learn node representation vectors, resulting in quadratic computational costs that are unaffordable for large-scale graph data. Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations.Facing this bottleneck, (1) we start by assigning each node a token list that is sampled by personalized PageRank (PPR) and then apply standard multi-head self-attention only on this list to compute its node representations. This PPR tokenization method decouples model training from complex graph topological information and makes heavy feature engineering offline and independent, such that mini-batch training of graph transformers is possible by loading each node's token list in batches. We further prove this PPR tokenization is viable as a graph convolution network with a fixed polynomial filter and jumping knowledge. However, only using personalized PageRank may limit information carried by a token list, which could not support different graph inductive biases for model training. To this end, (2) we rewire graphs by introducing multiple types of virtual connections through structure- and content-based super nodes that enable PPR tokenization to encode local and global contexts, long-range interaction, and heterophilous information into each node's token list, and then formalize our Virtual Connection Ranking based Graph Transformer (VCR-Graphormer). Overall, VCR-Graphormer only needs $O(m+klogk)$ complexity for graph tokenization as compared to $O(n^{3})$ of previous works. We also show that VCR-Graphormer outperforms the state-of-the-arts on node classification in 12 datasets. | [] | [] | VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections | [
"Dongqi Fu",
"Zhigang Hua",
"Yan Xie",
"Jin Fang",
"Si Zhang",
"Kaan Sancak",
"Hao Wu",
"Andrey Malevich",
"Jingrui He",
"Bo Long"
] | 18,601 | https://openreview.net/forum?id=SUUrkC3STJ |
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[] | Poster | [
"https://github.com/Linwei-Chen/Seg-Aliasing"
] | Despite recent advancements in semantic segmentation, where and what pixels are hard to segment remains largely unexplored.Existing research only separates an image into easy and hard regions and empirically observes the latter are associated with object boundaries.In this paper, we conduct a comprehensive analysis of hard pixel errors, categorizing them into three types: false responses, merging mistakes, and displacements. Our findings reveal a quantitative association between hard pixels and aliasing, which is distortion caused by the overlapping of frequency components in the Fourier domain during downsampling.To identify the frequencies responsible for aliasing, we propose using the equivalent sampling rate to calculate the Nyquist frequency, which marks the threshold for aliasing. Then, we introduce the aliasing score as a metric to quantify the extent of aliasing.While positively correlated with the proposed aliasing score, three types of hard pixels exhibit different patterns.Here, we propose two novel de-aliasing filter (DAF) and frequency mixing (FreqMix) modules to alleviate aliasing degradation by accurately removing or adjusting frequencies higher than the Nyquist frequency.The DAF precisely removes the frequencies responsible for aliasing before downsampling, while the FreqMix dynamically selects high-frequency components within the encoder block.Experimental results demonstrate consistent improvements in semantic segmentation and low-light instance segmentation tasks.The code will be released upon publication. | [] | [] | When Semantic Segmentation Meets Frequency Aliasing | [
"Linwei Chen",
"Lin Gu",
"Ying Fu"
] | 2403.09065 | 18,600 | https://openreview.net/forum?id=SYBdkHcXXK |
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[] | Poster | [] | Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating offensive and toxic content. Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging. Inspired by this observation, we introduce a framework called CRITIC that allows LLMs, which are essentially “black boxes” to validate and progressively amend their own outputs in a manner similar to human interaction with tools. More specifically, starting with an initial output, CRITIC interacts with appropriate tools to evaluate certain aspects of the text, and then revises the output based on the feedback obtained during this validation process. Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs. Meanwhile, our research highlights the crucial importance of external feedback in promoting the ongoing self-improvement of LLMs. | [] | [] | CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing | [
"Zhibin Gou",
"Zhihong Shao",
"Yeyun Gong",
"yelong shen",
"Yujiu Yang",
"Nan Duan",
"Weizhu Chen"
] | 2305.11738 | 18,586 | https://openreview.net/forum?id=Sx038qxjek |
|
[] | Spotlight Poster | [] | We introduce a deterministic variational formulation for training Bayesian last layer neural networks. This yields a sampling-free, single-pass model and loss that effectively improves uncertainty estimation. Our variational Bayesian last layer (VBLL) can be trained and evaluated with only quadratic complexity in last layer width, and is thus (nearly) computationally free to add to standard architectures. We experimentally investigate VBLLs, and show that they improve predictive accuracy, calibration, and out of distribution detection over baselines across both regression and classification. Finally, we investigate combining VBLL layers with variational Bayesian feature learning, yielding a lower variance collapsed variational inference method for Bayesian neural networks. | [] | [] | Variational Bayesian Last Layers | [
"James Harrison",
"John Willes",
"Jasper Snoek"
] | 2404.11599 | 18,585 | https://openreview.net/forum?id=Sx7BIiPzys |
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[] | Poster | [
"https://github.com/khalil-research/Neur2RO"
] | Robust optimization provides a mathematical framework for modeling and solving decision-making problems under worst-case uncertainty. This work addresses two-stage robust optimization (2RO) problems (also called adjustable robust optimization), wherein first-stage and second-stage decisions are made before and after uncertainty is realized, respectively. This results in a nested min-max-min optimization problem which is extremely challenging computationally, especially when the decisions are discrete. We propose Neur2RO, an efficient machine learning-driven instantiation of column-and-constraint generation (CCG), a classical iterative algorithm for 2RO. Specifically, we learn to estimate the value function of the second-stage problem via a novel neural network architecture that is easy to optimize over by design. Embedding our neural network into CCG yields high-quality solutions quickly as evidenced by experiments on two 2RO benchmarks, knapsack and capital budgeting. On small or easy instances, Neur2RO recovers solutions of nearly the same quality as state-of-the-art methods but is most advantageous on large-scale instances, where it finds better solutions on average. | [] | [] | Neur2RO: Neural Two-Stage Robust Optimization | [
"Justin Dumouchelle",
"Esther Julien",
"Jannis Kurtz",
"Elias Boutros Khalil"
] | 2310.04345 | 18,583 | https://openreview.net/forum?id=T5Xb0iGCCv |
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[] | Spotlight Poster | [] | Training generally capable agents that thoroughly explore their environment andlearn new and diverse skills is a long-term goal of robot learning. Quality DiversityReinforcement Learning (QD-RL) is an emerging research area that blends thebest aspects of both fields – Quality Diversity (QD) provides a principled formof exploration and produces collections of behaviorally diverse agents, whileReinforcement Learning (RL) provides a powerful performance improvementoperator enabling generalization across tasks and dynamic environments. ExistingQD-RL approaches have been constrained to sample efficient, deterministic off-policy RL algorithms and/or evolution strategies and struggle with highly stochasticenvironments. In this work, we, for the first time, adapt on-policy RL, specificallyProximal Policy Optimization (PPO), to the Differentiable Quality Diversity (DQD)framework and propose several changes that enable efficient optimization anddiscovery of novel skills on high-dimensional, stochastic robotics tasks. Our newalgorithm, Proximal Policy Gradient Arborescence (PPGA), achieves state-of-the-art results, including a 4x improvement in best reward over baselines on thechallenging humanoid domain. | [] | [] | Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning | [
"Sumeet Batra",
"Bryon Tjanaka",
"Matthew Christopher Fontaine",
"Aleksei Petrenko",
"Stefanos Nikolaidis",
"Gaurav S. Sukhatme"
] | 2305.13795 | 18,581 | https://openreview.net/forum?id=TFKIfhvdmZ |
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[] | Spotlight Poster | [] | The theories of offline and online reinforcement learning, despite having evolved in parallel, have recently started to see unification, and algorithms/concepts in one setting often have natural counterparts in the other. However, the notion of density ratio modeling, an emerging topic in offline RL, has been largely absent from online RL, perhaps for good reason: the very existence and boundedness of density ratios relies on a dataset with good coverage, but the core challenge in online RL is to collect such an exploratory dataset without having one to start.In this work we show—perhaps surprisingly—that density ratio-based algorithms have online counterparts. Assuming the mere existence of an exploratory distribution with good coverage, a structural condition known as coverability (Xie et al., 2023), we give an algorithm (GLOW) which performs sample-efficient online exploration under value-function and density-ratio realizability. GLOW addressesunbounded density ratios via careful use of truncation, and combines this with optimism to guide exploration. GLOW is computationally inefficient; we complement it with a more efficient counterpart, HYGLOW, for the Hybrid RL setting (Song et al., 2023) in which online RL is augmented with additional offline data. HYGLOW is derived as a special case of a novel meta-algorithm, H2O, which provides a provable black-box reduction from hybrid RL to offline RL. | [] | [] | Harnessing Density Ratios for Online Reinforcement Learning | [
"Philip Amortila",
"Dylan J Foster",
"Nan Jiang",
"Ayush Sekhari",
"Tengyang Xie"
] | 2401.09681 | 18,580 | https://openreview.net/forum?id=THJEa8adBn |
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[] | Poster | [] | Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. The sample's distance to the decision boundary is a natural measure of predictive uncertainty, but it is often intractable to compute, especially for complex decision boundaries formed in multiclass classification tasks.To address this issue, this paper proposes the *least disagree metric* (LDM), defined as the smallest probability of disagreement of the predicted label, and an estimator for LDM proven to be asymptotically consistent under mild assumptions. The estimator is computationally efficient and can be easily implemented for deep learning models using parameter perturbation. The LDM-based active learning is performed by querying unlabeled data with the smallest LDM. Experimental results show that our LDM-based active learning algorithm obtains state-of-the-art *overall* performance on all considered datasets and deep architectures. | [] | [] | Querying Easily Flip-flopped Samples for Deep Active Learning | [
"Seong Jin Cho",
"Gwangsu Kim",
"Junghyun Lee",
"Jinwoo Shin",
"Chang D. Yoo"
] | 2401.09787 | 18,579 | https://openreview.net/forum?id=THUBTfSAS2 |
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[] | Poster | [] | This paper introduces a novel approach to structural inference, combining a variational dynamics encoder with partial correlation coefficients. In contrast to prior methods, our approach leverages variational inference to encode node dynamics within latent variables, and structural reconstruction relies on the calculation of partial correlation coefficients derived from these latent variables.This unique design endows our method with scalability and extends its applicability to both one-dimensional and multi-dimensional feature spaces.Furthermore, by reorganizing latent variables according to temporal steps, our approach can effectively reconstruct directed graph structures. We validate our method through extensive experimentation on twenty datasets from a benchmark dataset and biological networks. Our results showcase the superior scalability, accuracy, and versatility of our proposed approach compared to existing methods.Moreover, experiments conducted on noisy data affirm the robustness of our method. | [] | [] | Structural Inference with Dynamics Encoding and Partial Correlation Coefficients | [
"Aoran Wang",
"Jun Pang"
] | 18,578 | https://openreview.net/forum?id=TKnzPdyeJu |
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[] | Poster | [
"https://github.com/winci-ai/INTL"
] | Whitening loss offers a theoretical guarantee against feature collapse in self-supervised learning (SSL) with joint embedding architectures. Typically, it involves a hard whitening approach, transforming the embedding and applying loss to the whitened output. In this work, we introduce Spectral Transformation (ST), a framework to modulate the spectrum of embedding and to seek for functions beyond whitening that can avoid dimensional collapse. We show that whitening is a special instance of ST by definition, and our empirical investigations unveil other ST instances capable of preventing collapse. Additionally, we propose a novel ST instance named IterNorm with trace loss (INTL). Theoretical analysis confirms INTL's efficacy in preventing collapse and modulating the spectrum of embedding toward equal-eigenvalues during optimization. Our experiments on ImageNet classification and COCO object detection demonstrate INTL's potential in learning superior representations. The code is available at https://github.com/winci-ai/INTL. | [] | [] | Modulate Your Spectrum in Self-Supervised Learning | [
"Xi Weng",
"Yunhao Ni",
"Tengwei Song",
"Jie Luo",
"Rao Muhammad Anwer",
"Salman Khan",
"Fahad Khan",
"Lei Huang"
] | 2305.16789 | 18,577 | https://openreview.net/forum?id=TKqMmKlmA7 |
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[] | Poster | [] | Text-guided diffusion models (TDMs) are widely applied but can fail unexpectedly. Common failures include: _(i)_ natural-looking text prompts generating images with the wrong content, or _(ii)_ different random samples of the latent variables that generate vastly different, and even unrelated, outputs despite being conditioned on the same text prompt. In this work, we aim to study and understand the failure modes of TDMs in more detail. To achieve this, we propose SAGE, the first adversarial search method on TDMs that systematically explores the discrete prompt space and the high-dimensional latent space, to automatically discover undesirable behaviors and failure cases in image generation. We use image classifiers as surrogate loss functions during searching, and employ human inspections to validate the identified failures. For the first time, our method enables efficient exploration of both the discrete and intricate human language space and the challenging latent space, overcoming the gradient vanishing problem. Then, we demonstrate the effectiveness of SAGE on five widely used generative models and reveal four typical failure modes that have not been systematically studied before: (1) We find a variety of natural text prompts that generate images failing to capture the semantics of input texts. We further discuss the underlying causes and potential solutions based on the results. (2) We find regions in the latent space that lead to distorted images independent of the text prompt, suggesting that parts of the latent space are not well-structured. (3) We also find latent samples that result in natural-looking images unrelated to the text prompt, implying a possible misalignment between the latent and prompt spaces. (4) By appending a single adversarial token embedding to any input prompts, we can generate a variety of specified target objects, with minimal impact on CLIP scores, demonstrating the fragility of language representations. | [] | [] | Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search | [
"Qihao Liu",
"Adam Kortylewski",
"Yutong Bai",
"Song Bai",
"Alan Yuille"
] | 2306.00974 | 18,575 | https://openreview.net/forum?id=TOWdQQgMJY |
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[] | Spotlight Poster | [] | We propose Feature-aligned N-BEATS as a domain-generalized time series forecasting model. It is a nontrivial extension of N-BEATS with doubly residual stacking principle (Oreshkin et al. [45]) into a representation learning framework. In particular, it revolves around marginal feature probability measures induced by the intricate composition of residual and feature extracting operators of N-BEATS in each stack and aligns them stack-wise via an approximate of an optimal transport distance referred to as the Sinkhorn divergence. The training loss consists of an empirical risk minimization from multiple source domains, i.e., forecasting loss, and an alignment loss calculated with the Sinkhorn divergence, which allows the model to learn invariant features stack-wise across multiple source data sequences while retaining N-BEATS’s interpretable design and forecasting power. Comprehensive experimental evaluations with ablation studies are provided and the corresponding results demonstrate the proposed model’s forecasting and generalization capabilities. | [] | [] | Feature-aligned N-BEATS with Sinkhorn divergence | [
"Joonhun Lee",
"Myeongho Jeon",
"Myungjoo Kang",
"Kyunghyun Park"
] | 18,573 | https://openreview.net/forum?id=TS8HoIWAPQ |
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[] | Spotlight Poster | [] | We consider the distributionally robust (DR) optimization problem with spectral risk-based uncertainty set and $f$-divergence penalty. This formulation includes common risk-sensitive learning objectives such as regularized condition value-at-risk (CVaR) and average top-$k$ loss. We present Prospect, a stochastic gradient-based algorithm that only requires tuning a single learning rate hyperparameter, and prove that it enjoys linear convergence for smooth regularized losses. This contrasts with previous algorithms that either require tuning multiple hyperparameters or potentially fail to converge due to biased gradient estimates or inadequate regularization. Empirically, we show that Prospect can converge 2-3x faster than baselines such as SGD and stochastic saddle-point methods on distribution shift and fairness benchmarks spanning tabular, vision, and language domains. | [] | [] | Distributionally Robust Optimization with Bias and Variance Reduction | [
"Ronak Mehta",
"Vincent Roulet",
"Krishna Pillutla",
"Zaid Harchaoui"
] | 2310.13863 | 18,571 | https://openreview.net/forum?id=TTrzgEZt9s |
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[] | Poster | [] | In this study, we delve into an emerging optimization challenge involving a black-box objective function that can only be gauged via a ranking oracle—a situation frequently encountered in real-world scenarios, especially when the function is evaluated by human judges. A prominent instance of such a situation is Reinforcement Learning with Human Feedback (RLHF), an approach recently employed to enhance the performance of Large Language Models (LLMs) using human guidance [Ouyang et al. 2022, Liu et al. 2023, OpenAI et al. 2022, Bai et al. 2022]. We introduce ZO-RankSGD, an innovative zeroth-order optimization algorithm designed to tackle this optimization problem, accompanied by theoretical assurances. Our algorithm utilizes a novel rank-based random estimator to determine the descent direction and guarantees convergence to a stationary point. Moreover, ZO-RankSGD is readily applicable to policy optimization problems in Reinforcement Learning (RL), particularly when only ranking oracles for the episode reward are available. Last but not least, we demonstrate the effectiveness of ZO-RankSGD in a novel application: improving the quality of images generated by a diffusion generative model with human ranking feedback. Throughout experiments, we found that ZO-RankSGD can significantly enhance the detail of generated images with only a few rounds of human feedback. Overall, our work advances the field of zeroth-order optimization by addressing the problem of optimizing functions with only ranking feedback, and offers a new and effective approach for aligning Artificial Intelligence (AI) with human intentions. | [] | [] | Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles | [
"Zhiwei Tang",
"Dmitry Rybin",
"Tsung-Hui Chang"
] | 2303.03751 | 18,570 | https://openreview.net/forum?id=TVDUVpgu9s |
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[] | Poster | [
"https://github.com/Lu-Feng/SelaVPR"
] | Recent studies show that vision models pre-trained in generic visual learning tasks with large-scale data can provide useful feature representations for a wide range of visual perception problems. However, few attempts have been made to exploit pre-trained foundation models in visual place recognition (VPR). Due to the inherent difference in training objectives and data between the tasks of model pre-training and VPR, how to bridge the gap and fully unleash the capability of pre-trained models for VPR is still a key issue to address. To this end, we propose a novel method to realize seamless adaptation of pre-trained models for VPR. Specifically, to obtain both global and local features that focus on salient landmarks for discriminating places, we design a hybrid adaptation method to achieve both global and local adaptation efficiently, in which only lightweight adapters are tuned without adjusting the pre-trained model. Besides, to guide effective adaptation, we propose a mutual nearest neighbor local feature loss, which ensures proper dense local features are produced for local matching and avoids time-consuming spatial verification in re-ranking. Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time. Our method ranks 1st on the leaderboard of MSLS place recognition challenge, and uses about only 3% retrieval runtime of the two-stage VPR method with RANSAC-based spatial verification. The code will be publicly available. | [] | [] | Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition | [
"Feng Lu",
"Lijun Zhang",
"Xiangyuan Lan",
"Shuting Dong",
"Yaowei Wang",
"Chun Yuan"
] | 2402.14505 | 18,569 | https://openreview.net/forum?id=TVg6hlfsKa |
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[] | Poster | [] | We present a systematic framework designed to enhance human-robot perception and collaboration through the integration of logical rules and Theory of Mind (ToM). Logical rules provide interpretable predictions and generalize well across diverse tasks, making them valuable for learning and decision-making. Leveraging the ToM for understanding others' mental states, our approach facilitates effective collaboration. In this paper, we employ logic rules derived from observational data to infer human goals and guide human-like agents. These rules are treated as latent variables, and a rule encoder is trained alongside a multi-agent system in the robot's mind. We assess the posterior distribution of latent rules using learned embeddings, representing entities and relations. Confidence scores for each rule indicate their consistency with observed data. Then, we employ a hierarchical reinforcement learning model with ToM to plan robot actions for assisting humans. Extensive experiments validate each component of our framework, and results on multiple benchmarks demonstrate that our model outperforms the majority of existing approaches. | [] | [] | Enhancing Human-AI Collaboration Through Logic-Guided Reasoning | [
"Chengzhi Cao",
"Yinghao Fu",
"Sheng Xu",
"Ruimao Zhang",
"Shuang Li"
] | 18,568 | https://openreview.net/forum?id=TWC4gLoAxY |
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[] | Poster | [] | Deep Metric Learning (DML) has long attracted the attention of the machine learning community as a key objective. Existing solutions concentrate on fine-tuning the pre-trained models on conventional image datasets. As a result of the success of recent pre-trained models derived from larger-scale datasets, it is challenging to adapt the model to the DML tasks in the local data domain while retaining the previously gained knowledge. In this paper, we investigate parameter-efficient methods for fine-tuning the pre-trained model for DML tasks. In particular, we propose a novel and effective framework based on learning Visual Prompts (VPT) in the pre-trained Vision Transformers (ViT). Based on the conventional proxy-based DML paradigm, we augment the proxy by incorporating the semantic information from the input image and the ViT, in which we optimize the visual prompts for each class. We demonstrate that our new approximations with semantic information are superior to representative capabilities, thereby improving metric learning performance. We conduct extensive experiments to demonstrate that our proposed framework is superior and efficient by evaluating popular DML benchmarks. In particular, we demonstrate that our fine-tuning method achieves comparable or even better performance than recent state-of-the-art full fine-tuning works of DML while tuning only a small percentage of total parameters. | [] | [] | Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning | [
"Li Ren",
"Chen Chen",
"Liqiang Wang",
"Kien A. Hua"
] | 2402.02340 | 18,567 | https://openreview.net/forum?id=TWVMVPx2wO |
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[] | Poster | [] | Transparent machine learning (ML) models are essential for ensuring interpretability and trustworthiness in decision-making systems, particularly in high-stakes domains such as healthcare, finance, and criminal justice. While transparent machine learning models have been proposed for classification and regression, time series forecasting presents some unique challenges for ensuring transparency. In particular, currently used bottom-up approaches that focus on the values of the time series at specific time points (usually regularly spaced) do not provide a holistic understanding of the entire time series. This limits the applicability of ML in many critical areas. To open up these domains for ML, we propose a top-down framework of bi-level transparency, which involves understanding the higher-level trends and the lower-level properties of the predicted time series. Applying this framework, we develop TIMEVIEW, a transparent ML model for time series forecasting based on static features, complemented with an interactive visualization tool. Through a series of experiments, we demonstrate the efficacy and interpretability of our approach, paving the way for more transparent and reliable applications of ML in various domains. | [] | [] | Towards Transparent Time Series Forecasting | [
"Krzysztof Kacprzyk",
"Tennison Liu",
"Mihaela van der Schaar"
] | 18,566 | https://openreview.net/forum?id=TYXtXLYHpR |
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[] | Poster | [] | Federated learning is a powerful paradigm for large-scale machine learning, but itfaces significant challenges due to unreliable network connections, slow commu-nication, and substantial data heterogeneity across clients. FedAvg and SCAFFOLD are two prominent algorithms to address these challenges. In particular,FedAvg employs multiple local updates before communicating with a centralserver, while SCAFFOLD maintains a control variable on each client to compen-sate for “client drift” in its local updates. Various methods have been proposedto enhance the convergence of these two algorithms, but they either make imprac-tical adjustments to algorithmic structure, or rely on the assumption of boundeddata heterogeneity. This paper explores the utilization of momentum to enhancethe performance of FedAvg and SCAFFOLD. When all clients participate in thetraining process, we demonstrate that incorporating momentum allows FedAvgto converge without relying on the assumption of bounded data heterogeneity evenusing a constant local learning rate. This is novel and fairly suprising as existinganalyses for FedAvg require bounded data heterogeneity even with diminishinglocal learning rates. In partial client participation, we show that momentum en-ables SCAFFOLD to converge provably faster without imposing any additionalassumptions. Furthermore, we use momentum to develop new variance-reducedextensions of FedAvg and SCAFFOLD, which exhibit state-of-the-art conver-gence rates. Our experimental results support all theoretical findings. | [] | [] | Momentum Benefits Non-iid Federated Learning Simply and Provably | [
"Ziheng Cheng",
"Xinmeng Huang",
"Pengfei Wu",
"Kun Yuan"
] | 2306.16504 | 18,565 | https://openreview.net/forum?id=TdhkAcXkRi |
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[] | Poster | [] | We propose an effective denoising diffusion model for generating high-resolution images (e.g., 1024$\times$512), trained on small-size image patches (e.g., 64$\times$64). We name our algorithm Patch-DM, in which a new feature collage strategy is designed to avoid the boundary artifact when synthesizing large-size images. Feature collage systematically crops and combines partial features of the neighboring patches to predict the features of a shifted image patch, allowing the seamless generation of the entire image due to the overlap in the patch feature space. Patch-DM produces high-quality image synthesis results on our newly collected dataset of nature images (1024$\times$512), as well as on standard benchmarks of LHQ(1024$\times$ 1024), FFHQ(1024$\times$ 1024) and on other datasets with smaller sizes (256$\times$256), including LSUN-Bedroom, LSUN-Church, and FFHQ. We compare our method with previous patch-based generation methods and achieve state-of-the-art FID scores on all six datasets. Further, Patch-DM also reduces memory complexity compared to the classic diffusion models. | [] | [] | Patched Denoising Diffusion Models For High-Resolution Image Synthesis | [
"Zheng Ding",
"Mengqi Zhang",
"Jiajun Wu",
"Zhuowen Tu"
] | 2308.01316 | 18,564 | https://openreview.net/forum?id=TgSRPRz8cI |
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[] | Poster | [] | Despite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i.e., generating content that deviates from facts seen during pretraining. We propose a simple decoding strategy for reducing hallucinations with pretrained LLMs that does not require conditioning on retrieved external knowledge nor additional fine-tuning. Our approach obtains the next-token distribution by contrasting the differences in logits obtained from projecting the later layers versus earlier layers to the vocabulary space, exploiting the fact that factual knowledge in an LLMs has generally been shown to be localized to particular transformer layers. We find that this **D**ecoding by C**o**ntrasting **La**yers (DoLa) approach is able to better surface factual knowledge and reduce the generation of incorrect facts. DoLa consistently improves the truthfulness across multiple choices tasks and open-ended generation tasks, for example improving the performance of LLaMA family models on TruthfulQA by 12-17% absolute points, demonstrating its potential in making LLMs reliably generate truthful facts. | [] | [] | DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models | [
"Yung-Sung Chuang",
"Yujia Xie",
"Hongyin Luo",
"Yoon Kim",
"James R. Glass",
"Pengcheng He"
] | 2309.03883 | 18,563 | https://openreview.net/forum?id=Th6NyL07na |
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[] | Spotlight Poster | [] | Modern language models can imitate complex patterns through few-shot learning, enabling them to complete challenging tasks without fine-tuning. However, imitation can also lead models to reproduce inaccuracies or harmful content if present in the context. We study harmful imitation through the lens of a model’s internal representations, and identify two related phenomena: overthinking and false induction heads. The first phenomenon, overthinking, appears when we decode predictions from intermediate layers, given correct vs. incorrect few-shot demonstrations. At early layers, both demonstrations induce similar model behavior, but the behavior diverges sharply at some “critical layer”, after which the accuracy given incorrect demonstrations progressively decreases. The second phenomenon, false induction heads, are a possible mechanistic cause of overthinking: these are heads in late layers that attend to and copy false information from previous demonstrations, and whose ablation reduces overthinking. Beyond scientific understanding, our results suggest that studying intermediate model computations could be a promising avenue for understanding and guarding against harmful model behaviors. | [] | [] | Overthinking the Truth: Understanding how Language Models Process False Demonstrations | [
"Danny Halawi",
"Jean-Stanislas Denain",
"Jacob Steinhardt"
] | 2307.09476 | 18,562 | https://openreview.net/forum?id=Tigr1kMDZy |
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[] | Poster | [] | Deep Neural Networks (DNNs), essential for diverse applications such as visual recognition and eldercare, often require a large amount of labeled data for training, making widespread deployment of DNNs a challenging task. Self-supervised learning (SSL) emerges as a promising approach, which leverages inherent patterns within data through diverse augmentations to train models without explicit labels. However, while SSL has shown notable advancements in accuracy, its high computation costs remain a daunting impediment, particularly for resource-constrained platforms. To address this problem, we introduce SimWnW, a similarity-based efficient self-supervised learning framework. By strategically removing less important regions in augmented images and feature maps, SimWnW not only reduces computation costs but also eliminates irrelevant features that might slow down the learning process, thereby accelerating model convergence. The experimental results show that SimWnW effectively reduces the amount of computation costs in self-supervised model training without compromising accuracy. Specifically, SimWnW yields up to 54\% and 51\% computation savings in training from scratch and transfer learning tasks, respectively. | [] | [] | Waxing-and-Waning: a Generic Similarity-based Framework for Efficient Self-Supervised Learning | [
"Sheng Li",
"Chao Wu",
"Ao Li",
"Yanzhi Wang",
"Xulong Tang",
"Geng Yuan"
] | 18,561 | https://openreview.net/forum?id=TilcG5C8bN |
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[] | Poster | [] | Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring potential noise and missing information. Furthermore, their local aggregation mechanism can lead to problems such as over-squashing and limited expressive power in capturing relevant graph structures. Existing solutions to these challenges have primarily relied on heuristic methods, often disregarding the underlying data distribution. Hence, devising principled approaches for learning to infer graph structures relevant to the given prediction task remains an open challenge. In this work, leveraging recent progress in exact and differentiable k-subset sampling, we devise probabilistically rewired MPNNs (PR-MPNNs), which learn to add relevant edges while omitting less beneficial ones. For the first time, our theoretical analysis explores how PR-MPNNs enhance expressive power, and we identify precise conditions under which they outperform purely randomized approaches. Empirically, we demonstrate that our approach effectively mitigates issues like over-squashing and under-reaching. In addition, on established real-world datasets, our method exhibits competitive or superior predictive performance compared to traditional MPNN models and recent graph transformer architectures. | [] | [] | Probabilistically Rewired Message-Passing Neural Networks | [
"Chendi Qian",
"Andrei Manolache",
"Kareem Ahmed",
"Zhe Zeng",
"Guy Van den Broeck",
"Mathias Niepert",
"Christopher Morris"
] | 2310.02156 | 18,559 | https://openreview.net/forum?id=Tj6Wcx7gVk |
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[] | Spotlight Poster | [] | Hierarchical world models can significantly improve model-based reinforcement learning (MBRL) and planning by enabling reasoning across multiple time scales. Nonetheless, the majority of state-of-the-art MBRL methods still employ flat, non-hierarchical models. We propose Temporal Hierarchies from Invariant Context Kernels (THICK), an algorithm that learns a world model hierarchy via discrete latent dynamics. The lower level of THICK updates parts of its latent state sparsely in time, forming invariant contexts. The higher level exclusively predicts situations involving context state changes. Our experiments demonstrate that THICK learns categorical, interpretable, temporal abstractions on the high level, while maintaining precise low-level predictions. Furthermore, we show that the emergent hierarchical predictive model seamlessly enhances the abilities of MBRL or planning methods. We believe that THICK contributes to the further development of hierarchical, context-conditioned, event-predictive world models that can enhance planning and reasoning abilities and produce more human-like behavior. | [] | [] | Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics | [
"Christian Gumbsch",
"Noor Sajid",
"Georg Martius",
"Martin V. Butz"
] | 18,558 | https://openreview.net/forum?id=TjCDNssXKU |
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[] | Poster | [] | Proximal causal learning is a powerful framework for identifying the causal effect under the existence of unmeasured confounders. Within this framework, the doubly robust (DR) estimator was derived and has shown its effectiveness in estimation, especially when the model assumption is violated. However, the current form of the DR estimator is restricted to binary treatments, while the treatments can be continuous in many real-world applications. The primary obstacle to continuous treatments resides in the delta function present in the original DR estimator, making it infeasible in causal effect estimation and introducing a heavy computational burden in nuisance function estimation. To address these challenges, we propose a kernel-based DR estimator that can well handle continuous treatments for proximal causal learning. Equipped with its smoothness, we show that its oracle form is a consistent approximation of the influence function. Further, we propose a new approach to efficiently solve the nuisance functions. We then provide a comprehensive convergence analysis in terms of the mean square error. We demonstrate the utility of our estimator on synthetic datasets and real-world applications. | [] | [] | Doubly Robust Proximal Causal Learning for Continuous Treatments | [
"Yong Wu",
"Yanwei Fu",
"Shouyan Wang",
"Xinwei Sun"
] | 2309.12819 | 18,557 | https://openreview.net/forum?id=TjGJFkU3xL |
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[] | Poster | [] | We introduce the use of generative adversarial learning to compute equilibria in general game-theoretic settings, specifically the generalized Nash equilibrium (GNE) in pseudo-games, and its specific instantiation as the competitive equilibrium (CE) in Arrow-Debreu competitive economies. Pseudo-games are a generalization of games in which players' actions affect not only the payoffs of other players but also their feasible action spaces. Although the computation of GNE and CE is intractable in the worst-case, i.e., PPAD-hard, in practice, many applications only require solutions with high accuracy in expectation over a distribution of problem instances. We introduce Generative Adversarial Equilibrium Solvers (GAES): a family of generative adversarial neural networks that can learn GNE and CE from only a sample of problem instances. We provide computational and sample complexity bounds for Lipschitz-smooth function approximators in a large class of concave pseudo-games, and apply the framework to finding Nash equilibria in normal-form games, CE in Arrow-Debreu competitive economies, and GNE in an environmental economic model of the Kyoto mechanism. | [] | [] | Generative Adversarial Equilibrium Solvers | [
"Denizalp Goktas",
"David C. Parkes",
"Ian Gemp",
"Luke Marris",
"Georgios Piliouras",
"Romuald Elie",
"Guy Lever",
"Andrea Tacchetti"
] | 2302.06607 | 18,553 | https://openreview.net/forum?id=TlyiaPXaVN |
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[] | Poster | [] | With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation for LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs.The proposed solution has two innovative components: 1) **Relative Importance and Activations** (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs; and 2) **Channel Permutation**, a new approach to maximally preserve important weights under N:M sparsity.The proposed two components can be readily combined to further enhance the N:M structuredly pruned LLMs.Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M structured pruning with channel permutation can even outperform the original LLaMA2 70B on zero-shot tasks, together with practical speed-up on specific hardware. | [] | [] | Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models | [
"Yingtao Zhang",
"Haoli Bai",
"Haokun Lin",
"Jialin Zhao",
"Lu Hou",
"Carlo Vittorio Cannistraci"
] | 18,549 | https://openreview.net/forum?id=Tr0lPx9woF |
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[] | Spotlight Poster | [] | Shannon, in his seminal paper introducing information theory, divided the communication into three levels: technical, semantic, and effectivenss. While the technical level is concerned with accurate reconstruction of transmitted symbols, the semantic and effectiveness levels deal with the inferred meaning and its effect on the receiver. Thanks to telecommunications, the first level problem has produced great advances like the internet. Large Language Models (LLMs) make some progress towards the second goal, but the third level still remains largely untouched. The third problem deals with predicting and optimizing communication for desired receiver behavior. LLMs, while showing wide generalization capabilities across a wide range of tasks, are unable to solve for this. One reason for the underperformance could be a lack of ``behavior tokens'' in LLMs' training corpora. Behavior tokens define receiver behavior over a communication, such as shares, likes, clicks, purchases, retweets, \textit{etc}. While preprocessing data for LLM training, behavior tokens are often removed from the corpora as noise. Therefore, in this paper, we make some initial progress towards reintroducing behavior tokens in LLM training. The trained models, other than showing similar performance to LLMs on content understanding tasks, show generalization capabilities on behavior simulation, content simulation, behavior understanding, and behavior domain adaptation. Using a wide range of tasks on two corpora, we show results on all these capabilities. We call these models Large Content and Behavior Models (LCBMs). Further, to spur more research on LCBMs, we release our new Content Behavior Corpus (CBC), a repository containing communicator, message, and corresponding receiver behavior. | [] | [] | Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior | [
"Ashmit Khandelwal",
"Aditya Agrawal",
"Aanisha Bhattacharyya",
"Yaman Kumar",
"Somesh Singh",
"Uttaran Bhattacharya",
"Ishita Dasgupta",
"Stefano Petrangeli",
"Rajiv Ratn Shah",
"Changyou Chen",
"Balaji Krishnamurthy"
] | 2309.00359 | 18,547 | https://openreview.net/forum?id=TrKq4Wlwcz |
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[] | Poster | [] | Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs. Sparse BNNs have been investigated for efficient inference, typically by either slowly introducing sparsity throughout the training or by post-training compression of dense BNNs. The dilemma of how to cut down massive training costs remains, particularly given the requirement to learn about the uncertainty. To solve this challenge, we introduce Sparse Subspace Variational Inference (SSVI), the first fully sparse BNN framework that maintains a consistently sparse Bayesian model throughout the training and inference phases. Starting from a randomly initialized low-dimensional sparse subspace, our approach alternately optimizes the sparse subspace basis selection and its associated parameters. While basis selection is characterized as a non-differentiable problem, we approximate the optimal solution with a removal-and-addition strategy, guided by novel criteria based on weight distribution statistics. Our extensive experiments show that SSVI sets new benchmarks in crafting sparse BNNs, achieving, for instance, a 10-20× compression in model size with under 3\% performance drop, and up to 20× FLOPs reduction during training. Remarkably, SSVI also demonstrates enhanced robustness to hyperparameters, reducing the need for intricate tuning in VI and occasionally even surpassing VI-trained dense BNNs. | [] | [] | Training Bayesian Neural Networks with Sparse Subspace Variational Inference | [
"Junbo Li",
"Zichen Miao",
"Qiang Qiu",
"Ruqi Zhang"
] | 2402.11025 | 18,545 | https://openreview.net/forum?id=TskzCtpMEO |
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[] | Poster | [] | This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a fundamental large model, or fine-tunes a pre-trained LLM for TS data; TS-for-LLM (data-centric) converts TS into a model-friendly representation to enable the pre-trained LLM to handle TS data. Given the lack of data, limited resources, semantic context requirements, and so on, this work focuses on TS-for-LLM, where we aim to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM. The proposed method is named TEST. It first tokenizes TS, builds an encoder to embed TS via instance-wise, feature-wise, and text-prototype-aligned contrast, where the TS embedding space is aligned to LLM’s embedding layer space, then creates soft prompts to make LLM more open to that embeddings, and finally implements TS tasks using the frozen LLM. We also demonstrate the feasibility of TS-for-LLM through theory and experiments. Experiments are carried out on TS classification, forecasting, and representation tasks using eight frozen LLMs with various structures and sizes. The results show that the pre-trained LLM with TEST strategy can achieve better or comparable performance than today's SOTA TS models, and offers benefits for few-shot and generalization. By treating LLM as the pattern machine, TEST can endow LLM's ability to process TS data without compromising language ability. We hope that this study will serve as a foundation for future work to support TS+LLM progress. | [] | [] | TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series | [
"Chenxi Sun",
"Hongyan Li",
"Yaliang Li",
"Shenda Hong"
] | 2308.08241 | 18,544 | https://openreview.net/forum?id=Tuh4nZVb0g |
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[] | Poster | [] | Adversarial attacks have been a looming and unaddressed threat in the industry. However, through a decade-long history of the robustness evaluation literature, we have learned that mounting a strong or optimal attack is challenging. It requires both machine learning and domain expertise. In other words, the white-box threat model, religiously assumed by a large majority of the past literature, is unrealistic. In this paper, we propose a new practical threat model where the adversary relies on transfer attacks through publicly available surrogate models. We argue that this setting will become the most prevalent for security-sensitive applications in the future. We evaluate the transfer attacks in this setting and propose a specialized defense method based on a game-theoretic perspective. The defenses are evaluated under 24 public models and 11 attack algorithms across three datasets (CIFAR-10, CIFAR-100, and ImageNet). Under this threat model, our defense, PubDef, outperforms the state-of-the-art white-box adversarial training by a large margin with almost no loss in the normal accuracy. For instance, on ImageNet, our defense achieves 62% accuracy under the strongest transfer attack vs only 36% of the best adversarially trained model. Its accuracy when not under attack is only 2% lower than that of an undefended model (78% vs 80%). | [] | [] | PubDef: Defending Against Transfer Attacks From Public Models | [
"Chawin Sitawarin",
"Jaewon Chang",
"David Huang",
"Wesson Altoyan",
"David Wagner"
] | 2310.17645 | 18,543 | https://openreview.net/forum?id=Tvwf4Vsi5F |
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[] | Spotlight Poster | [] | Recent studies revealed that using third-party models may lead to backdoor threats, where adversaries can maliciously manipulate model predictions based on backdoors implanted during model training. Arguably, backdoor trigger inversion (BTI), which generates trigger patterns of given benign samples for a backdoored model, is the most critical module for backdoor defenses used in these scenarios. With BTI, defenders can remove backdoors by fine-tuning based on generated poisoned samples with ground-truth labels or deactivate backdoors by removing trigger patterns during the inference process. However, we find that existing BTI methods suffer from relatively poor performance, $i.e.$, their generated triggers are significantly different from the ones used by the adversaries even in the feature space. We argue that it is mostly because existing methods require to 'extract' backdoor features at first, while this task is very difficult since defenders have no information ($e.g.$, trigger pattern or target label) about poisoned samples. In this paper, we explore BTI from another perspective where we decouple benign features instead of decoupling backdoor features directly. Specifically, our method consists of two main steps, including \textbf{(1)} decoupling benign features and \textbf{(2)} trigger inversion by minimizing the differences between benign samples and their generated poisoned version in decoupled benign features while maximizing the differences in remaining backdoor features. In particular, our method is more efficient since it doesn't need to `scan' all classes to speculate the target label, as required by existing BTI. We also exploit our BTI module to further design backdoor-removal and pre-processing-based defenses. Extensive experiments on benchmark datasets demonstrate that our defenses can reach state-of-the-art performances. | [] | [] | Towards Reliable and Efficient Backdoor Trigger Inversion via Decoupling Benign Features | [
"Xiong Xu",
"Kunzhe Huang",
"Yiming Li",
"Zhan Qin",
"Kui Ren"
] | 18,542 | https://openreview.net/forum?id=Tw9wemV6cb |
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[] | Poster | [] | Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks. However, the cross-task correlations that are learned in the unstructured feature space can be extremely noisy and susceptible to overfitting, consequently hurting performance. We propose to address this problem by introducing a structured 3D-aware regularizer which interfaces multiple tasks through the projection of features extracted from an image encoder to a shared 3D feature space and decodes them into their task output space through differentiable rendering. We show that the proposed method is architecture agnostic and can be plugged into various prior multi-task backbones to improve their performance; as we evidence using standard benchmarks NYUv2 and PASCAL-Context. | [] | [] | Multi-task Learning with 3D-Aware Regularization | [
"Wei-Hong Li",
"Steven McDonagh",
"Ales Leonardis",
"Hakan Bilen"
] | 2310.00986 | 18,541 | https://openreview.net/forum?id=TwBY17Hgiy |
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[] | Spotlight Poster | [] | With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowd workers' confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an optimization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model performance compared to existing value-aligned algorithms. Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with collected human preferences, significantly improving its helpfulness and harmlessness according to human evaluations.Warning: This paper contains example data that may be offensive or harmful. | [] | [] | Safe RLHF: Safe Reinforcement Learning from Human Feedback | [
"Josef Dai",
"Xuehai Pan",
"Ruiyang Sun",
"Jiaming Ji",
"Xinbo Xu",
"Mickel Liu",
"Yizhou Wang",
"Yaodong Yang"
] | 2310.12773 | 18,540 | https://openreview.net/forum?id=TyFrPOKYXw |
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[] | Spotlight Poster | [] | Learning arguably involves the discovery and memorization of abstract rules. The aim of this paper is to study associative memory mechanisms. Our model is based on high-dimensional matrices consisting of outer products of embeddings, which relates to the inner layers of transformer language models. We derive precise scaling laws with respect to sample size and parameter size, and discuss the statistical efficiency of different estimators, including optimization-based algorithms. We provide extensive numerical experiments to validate and interpret theoretical results, including fine-grained visualizations of the stored memory associations. | [] | [] | Scaling Laws for Associative Memories | [
"Vivien Cabannes",
"Elvis Dohmatob",
"Alberto Bietti"
] | 2310.02984 | 18,538 | https://openreview.net/forum?id=Tzh6xAJSll |
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[] | Poster | [] | In multi-task reinforcement learning (RL) under Markov decision processes (MDPs), the presence of shared latent structures among multiple MDPs has been shown to yield significant benefits to the sample efficiency compared to single-task RL. In this paper, we investigate whether such a benefit can extend to more general sequential decision making problems, such as partially observable MDPs (POMDPs) and more general predictive state representations (PSRs). The main challenge here is that the large and complex model space makes it hard to identify what types of common latent structure of multi-task PSRs can reduce the model complexity and improve sample efficiency. To this end, we posit a {\em joint model class} for tasks and use the notion of $\eta$-bracketing number to quantify its complexity; this number also serves as a general metric to capture the similarity of tasks and thus determines the benefit of multi-task over single-task RL. We first study upstream multi-task learning over PSRs, in which all tasks share the same observation and action spaces. We propose a provably efficient algorithm UMT-PSR for finding near-optimal policies for all PSRs, and demonstrate that the advantage of multi-task learning manifests if the joint model class of PSRs has a smaller $\eta$-bracketing number compared to that of individual single-task learning. We also provide several example multi-task PSRs with small $\eta$-bracketing numbers, which reap the benefits of multi-task learning. We further investigate downstream learning, in which the agent needs to learn a new target task that shares some commonalities with the upstream tasks via a similarity constraint. By exploiting the learned PSRs from the upstream, we develop a sample-efficient algorithm that provably finds a near-optimal policy. Upon specialization to the examples used to elucidate the $\eta$-bracketing numbers, our downstream results further highlight the benefit compared to directly learning the target PSR without upstream information. Ours is the first theoretical study that quantifies the benefits of multi-task RL with PSRs over its single-task counterpart. | [] | [] | Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes | [
"Ruiquan Huang",
"Yuan Cheng",
"Jing Yang",
"Vincent Tan",
"Yingbin Liang"
] | 2310.13550 | 18,535 | https://openreview.net/forum?id=U6Qulbv2qT |
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[] | Poster | [] | Prior methods that tackle the problem of generalizable object pose estimation highly rely on having dense views of the unseen object. By contrast, we address the scenario where only a single reference view of the object is available. Our goal then is to estimate the relative object pose between this reference view and a query image that depicts the object in a different pose. In this scenario, robust generalization is imperative due to the presence of unseen objects during testing and the large-scale object pose variation between the reference and the query. To this end, we present a new hypothesis-and-verification framework, in which we generate and evaluate multiple pose hypotheses, ultimately selecting the most reliable one as the relative object pose. To measure reliability, we introduce a 3D-aware verification that explicitly applies 3D transformations to the 3D object representations learned from the two input images. Our comprehensive experiments on the Objaverse, LINEMOD, and CO3D datasets evidence the superior accuracy of our approach in relative pose estimation and its robustness in large-scale pose variations, when dealing with unseen objects. | [] | [] | 3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation | [
"Chen Zhao",
"Tong Zhang",
"Mathieu Salzmann"
] | 2310.03534 | 18,534 | https://openreview.net/forum?id=U6hEOZlDf5 |
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[] | Poster | [] | Perception is crucial in the realm of autonomous driving systems, where bird's eye view (BEV)-based architectures have recently reached state-of-the-art performance. The desirability of self-supervised representation learning stems from the expensive and laborious process of annotating 2D and 3D data. Although previous research has investigated pretraining methods for both LiDAR and camera-based 3D object detection, a unified pretraining framework for multimodal BEV perception is missing. In this study, we introduce CALICO, a novel framework that applies contrastive objectives to both LiDAR and camera backbones. Specifically, CALICO incorporates two stages: point-region contrast (PRC) and region-aware distillation (RAD). PRC better balances the region- and scene-level representation learning on the LiDAR modality and offers significant performance improvement compared to existing methods. RAD effectively achieves contrastive distillation on our self-trained teacher model. CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements. Notably, CALICO outperforms the baseline method by 10.5\% and 8.6\% on NDS and mAP. Moreover, CALICO boosts the robustness of multimodal 3D object detection against adversarial attacks and corruption. Additionally, our framework can be tailored to different backbones and heads, positioning it as a promising approach for multimodal BEV perception. | [] | [] | CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception | [
"Jiachen Sun",
"Haizhong Zheng",
"Qingzhao Zhang",
"Atul Prakash",
"Zhuoqing Mao",
"Chaowei Xiao"
] | 2306.00349 | 18,532 | https://openreview.net/forum?id=U7iiF79kI3 |
|
[] | Spotlight Poster | [
"https://github.com/bigai-ai/civrealm"
] | The generalization of decision-making agents encompasses two fundamental elements: learning from past experiences and reasoning in novel contexts. However, the predominant emphasis in most interactive environments is on learning, often at the expense of complexity in reasoning. In this paper, we introduce CivRealm, an environment inspired by the Civilization game. Civilization's profound alignment with human history and society necessitates sophisticated learning, while its ever-changing situations demand strong reasoning to generalize. Particularly, CivRealm sets up an imperfect-information general-sum game with a changing number of players; it presents a plethora of complex features, challenging the agent to deal with open-ended stochastic environments that require diplomacy and negotiation skills. Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning. To catalyze further research, we present initial results for both paradigms. The canonical RL-based agents exhibit reasonable performance in mini-games, whereas both RL- and LLM-based agents struggle to make substantial progress in the full game. Overall, CivRealm stands as a unique learning and reasoning challenge for decision-making agents. The code is available at https://github.com/bigai-ai/civrealm. | [] | [] | CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents | [
"Siyuan Qi",
"Shuo Chen",
"Yexin Li",
"Xiangyu Kong",
"Junqi Wang",
"Bangcheng Yang",
"Pring Wong",
"Yifan Zhong",
"Xiaoyuan Zhang",
"Zhaowei Zhang",
"Nian Liu",
"Yaodong Yang",
"Song-Chun Zhu"
] | 2401.10568 | 18,531 | https://openreview.net/forum?id=UBVNwD3hPN |
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[] | Poster | [] | Human motion driven control (HMDC) is an effective approach for generating natural and compelling robot motions while preserving high-level semantics. However, establishing the correspondence between humans and robots with different body structures is not straightforward due to the mismatches in kinematics and dynamics properties, which causes intrinsic ambiguity to the problem. Many previous algorithms approach this motion retargeting problem with unsupervised learning, which requires the prerequisite skill sets. However, it will be extremely costly to learn all the skills without understanding the given human motions, particularly for high-dimensional robots. In this work, we introduce CrossLoco, a guided unsupervised reinforcement learning framework that simultaneously learns robot skills and their correspondence to human motions. Our key innovation is to introduce a cycle-consistency-based reward term designed to maximize the mutual information between human motions and robot states. We demonstrate that the proposed framework can generate compelling robot motions by translating diverse human motions, such as running, hopping, and dancing. We quantitatively compare our CrossLoco against the manually engineered and unsupervised baseline algorithms along with the ablated versions of our framework and demonstrate that our method translates human motions with better accuracy, diversity, and user preference. We also showcase its utility in other applications, such as synthesizing robot movements from | [] | [] | CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning | [
"Tianyu Li",
"Hyunyoung Jung",
"Matthew Gombolay",
"Yong Cho",
"Sehoon Ha"
] | 2309.17046 | 18,530 | https://openreview.net/forum?id=UCfz492fM8 |
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[] | Poster | [] | Robust generalization is a major challenge in deep learning, particularly when the number of trainable parameters is very large. In general, it is very difficult to know if the network has memorized a particular set of examples or understood the underlying rule (or both). Motivated by this challenge, we study an interpretable model where generalizing representations are understood analytically, and are easily distinguishable from the memorizing ones. Namely, we consider two-layer neural networks trained on modular arithmetic tasks where ($\\xi \\cdot 100\\%$) of labels are corrupted (*i.e.* some results of the modular operations in the training set are incorrect). We show that (i) it is possible for the network to memorize the corrupted labels *and* achieve $100\\%$ generalization at the same time; (ii) the memorizing neurons can be identified and pruned, lowering the accuracy on corrupted data and improving the accuracy on uncorrupted data; (iii) regularization methods such as weight decay, dropout and BatchNorm force the network to ignore the corrupted data during optimization, and achieve $100\\%$ accuracy on the uncorrupted dataset; and (iv) the effect of these regularization methods is ("mechanistically") interpretable: weight decay and dropout force all the neurons to learn generalizing representations, while BatchNorm de-amplifies the output of memorizing neurons and amplifies the output of the generalizing ones. Finally, we show that in the presence of regularization, the training dynamics involves two consecutive stages: first, the network undergoes the *grokking* dynamics reaching high train *and* test accuracy; second, it unlearns the memorizing representations, where train accuracy suddenly jumps from $100\\%$ to $100 (1-\\xi)\\%$. | [] | [] | To Grok or not to Grok: Disentangling Generalization and Memorization on Corrupted Algorithmic Datasets | [
"Darshil Doshi",
"Aritra Das",
"Tianyu He",
"Andrey Gromov"
] | 2310.13061 | 18,529 | https://openreview.net/forum?id=UHjE5v5MB7 |
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[] | Poster | [] | Causal explanations of the predictions of NLP systems are essential to ensure safety and establish trust. Yet, existing methods often fall short of explaining model predictions effectively or efficiently and are often model-specific. In this paper, we address model-agnostic explanations, proposing two approaches for counterfactual (CF) approximation. The first approach is CF generation, where a large language model (LLM) is prompted to change a specific text concept while keeping confounding concepts unchanged. While this approach is demonstrated to be very effective, applying LLM at inference-time is costly. We hence present a second approach based on matching, and propose a method that is guided by an LLM at training-time and learns a dedicated embedding space. This space is faithful to a given causal graph and effectively serves to identify matches that approximate CFs. After showing theoretically that approximating CFs is required in order to construct faithful explanations, we benchmark our approaches and explain several models, including LLMs with billions of parameters. Our empirical results demonstrate the excellent performance of CF generation models as model-agnostic explainers. Moreover, our matching approach, which requires far less test-time resources, also provides effective explanations, surpassing many baselines. We also find that Top-K techniques universally improve every tested method. Finally, we showcase the potential of LLMs in constructing new benchmarks for model explanation and subsequently validate our conclusions. Our work illuminates new pathways for efficient and accurate approaches to interpreting NLP systems. | [] | [] | Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals | [
"Yair Ori Gat",
"Nitay Calderon",
"Amir Feder",
"Alexander Chapanin",
"Amit Sharma",
"Roi Reichart"
] | 2310.00603 | 18,527 | https://openreview.net/forum?id=UMfcdRIotC |
|
[] | Poster | [] | We consider the problem of source-free unsupervised category-level 3D pose estimation from only RGB images to an non-annotated and unlabelled target domain without any access to source domain data or annotations during adaptation. Collecting and annotating real world 3D data and corresponding images is laborious, expensive yet unavoidable process since even 3D pose domain adaptation methods require 3D data in the target domain. We introduce a method which is capable of adapting to a nuisance ridden target domain without any 3D data or annotations. We represent object categories as simple cuboid meshes, and harness a generative model of neural feature activations modeled as a von Mises Fisher distribution at each mesh vertex learnt using differential rendering. We focus on individual mesh vertex features and iteratively update them based on their proximity to corresponding features in the target domain. Our key insight stems from the observation that specific object subparts remain stable across out-of-domain (OOD) scenarios, enabling strategic utilization of these invariant subcomponents for effective model updates. Our model is then trained in an EM fashion alternating between updating the vertex features and feature extractor. We show that our method simulates fine-tuning on a global-pseudo labelled dataset under mild assumptions which converges to the target domain asymptotically. Through extensive empirical validation, we demonstrate the potency of our simple approach in addressing the domain shift challenge and significantly enhancing pose estimation accuracy. By accentuating robust and less changed object subcomponents, our framework contributes to the evolution of UDA techniques in the context of 3D pose estimation using only images from the target domain. | [] | [] | Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation | [
"Prakhar Kaushik",
"Aayush Mishra",
"Adam Kortylewski",
"Alan Yuille"
] | 2401.10848 | 18,526 | https://openreview.net/forum?id=UPvufoBAIs |
|
[] | Poster | [] | Regularized reinforcement learning (RL), particularly the entropy-regularized kind, has gained traction in optimal control and inverse RL. While standard unregularized RL methods remain unaffected by changes in the number of actions, we show that it can severely impact their regularized counterparts. This paper demonstrates the importance of decoupling the regularizer from the action space: that is, to maintain a consistent level of regularization regardless of how many actions are involved to avoid over-regularization. Whereas the problem can be avoided by introducing a task-specific temperature parameter, it is often undesirable and cannot solve the problem when action spaces are state-dependent. In the state-dependent action context, different states with varying action spaces are regularized inconsistently. We introduce two solutions: a static temperature selection approach and a dynamic counterpart, universally applicable where this problem arises. Implementing these changes improves performance on the DeepMind control suite in static and dynamic temperature regimes and a biological design task. | [] | [] | Decoupling regularization from the action space | [
"Sobhan Mohammadpour",
"Emma Frejinger",
"Pierre-Luc Bacon"
] | 18,524 | https://openreview.net/forum?id=UaMgmoKEBj |
||
[] | Poster | [] | Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation sampling (SDS), a methodology of using pretrained text-to-2D diffusion models to optimize a neural radiance field (NeRF) in a zero-shot setting. However, the lack of 3D awareness in the 2D diffusion model often destabilizes previous methods from generating a plausible 3D scene. To address this issue, we propose 3DFuse, a novel framework that incorporates 3D awareness into the pretrained 2D diffusion model, enhancing the robustness and 3D consistency of score distillation-based methods. Specifically, we introduce a consistency injection module which constructs a 3D point cloud from the text prompt and utilizes its projected depth map at given view as a condition for the diffusion model. The 2D diffusion model, through its generative capability, robustly infers dense structure from the sparse point cloud depth map and generates a geometrically consistent and coherent 3D scene. We also introduce a new technique called semantic coding that reduces semantic ambiguity of the text prompt for improved results. Our method can be easily adapted to various text-to-3D baselines, and we experimentally demonstrate how our method notably enhances the 3D consistency of generated scenes in comparison to previous baselines, achieving state-of-the-art performance in geometric robustness and fidelity. | [] | [] | Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation | [
"Junyoung Seo",
"Wooseok Jang",
"Min-Seop Kwak",
"Hyeonsu Kim",
"Jaehoon Ko",
"Junho Kim",
"Jin-Hwa Kim",
"Jiyoung Lee",
"Seungryong Kim"
] | 2303.07937 | 18,523 | https://openreview.net/forum?id=UbxWjq0UO2 |
|
[] | Poster | [] | Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, it is critical that docking methods generalize well across the proteome. However, existing benchmarks fail to rigorously assess generalizability. Therefore, we develop DockGen, a new benchmark based on the ligand-binding domains of proteins, and we show that machine learning-based docking models have very weak generalization abilities even when combined with various data augmentation strategies. Instead, we propose Confidence Bootstrapping, a new training paradigm that solely relies on the interaction between a diffusion and a confidence model. Unlike previous self-training methods from other domains, we directly exploit the multi-resolution generation process of diffusion models using rollouts and confidence scores to reduce the generalization gap. We demonstrate that Confidence Bootstrapping significantly improves the ability of ML-based docking methods to dock to unseen protein classes, edging closer to accurate and generalizable blind docking methods. | [] | [] | Deep Confident Steps to New Pockets: Strategies for Docking Generalization | [
"Gabriele Corso",
"Arthur Deng",
"Nicholas Polizzi",
"Regina Barzilay",
"Tommi S. Jaakkola"
] | 2402.18396 | 18,522 | https://openreview.net/forum?id=UfBIxpTK10 |
|
[] | Spotlight Poster | [] | Diffusion models have demonstrated remarkable capabilities in image synthesis and related generative tasks. Nevertheless, their practicality for low-latency real-world applications is constrained by substantial computational costs and latency issues. Quantization is a dominant way to compress and accelerate diffusion models, where post-training quantization (PTQ) and quantization-aware training (QAT) are two main approaches, each bearing its own properties. While PTQ exhibits efficiency in terms of both time and data usage, it may lead to diminished performance in low bit-width settings. On the other hand, QAT can help alleviate performance degradation but comes with substantial demands on computational and data resources. To capitalize on the advantages while avoiding their respective drawbacks, we introduce a data-free, quantization-aware and parameter-efficient fine-tuning framework for low-bit diffusion models, dubbed EfficientDM, to achieve QAT-level performance with PTQ-like efficiency. Specifically, we propose a quantization-aware variant of the low-rank adapter (QALoRA) that can be merged with model weights and jointly quantized to low bit-width. The fine-tuning process distills the denoising capabilities of the full-precision model into its quantized counterpart, eliminating the requirement for training data. To further enhance performance, we introduce scale-aware optimization to address ineffective learning of QALoRA due to variations in weight quantization scales across different layers. We also employ temporal learned step-size quantization to handle notable variations in activation distributions across denoising steps. Extensive experimental results demonstrate that our method significantly outperforms previous PTQ-based diffusion models while maintaining similar time and data efficiency. Specifically, there is only a marginal $0.05$ sFID increase when quantizing both weights and activations of LDM-4 to 4-bit on ImageNet $256\times256$. Compared to QAT-based methods, our EfficientDM also boasts a $16.2\times$ faster quantization speed with comparable generation quality, rendering it a compelling choice for practical applications. | [] | [] | EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models | [
"Yefei He",
"Jing Liu",
"Weijia Wu",
"Hong Zhou",
"Bohan Zhuang"
] | 2310.03270 | 18,521 | https://openreview.net/forum?id=UmMa3UNDAz |
|
[] | Poster | [] | This work introduces Video Diffusion Transformer (VDT), which pioneers the use of transformers in diffusion-based video generation.It features transformer blocks with modularized temporal and spatial attention modules to leverage the rich spatial-temporal representation inherited in transformers.Additionally, we propose a unified spatial-temporal mask modeling mechanism, seamlessly integrated with the model, to cater to diverse video generation scenarios.VDT offers several appealing benefits.**(1)** It excels at capturing temporal dependencies to produce temporally consistent video frames and even simulate the physics and dynamics of 3D objects over time.**(2)** It facilitates flexible conditioning information, \eg, simple concatenation in the token space, effectively unifying different token lengths and modalities.**(3)** Pairing with our proposed spatial-temporal mask modeling mechanism, it becomes a general-purpose video diffuser for harnessing a range of tasks, including unconditional generation, video prediction, interpolation, animation, and completion, etc.%Extensive experiments on these tasks spanning various scenarios, including autonomous driving, natural weather, human action, and physics-based simulation, demonstrate the effectiveness of VDT.% Moreover, we provide a comprehensive study on the capabilities of VDT in capturing accurate temporal dependencies, handling conditioning information, and the spatial-temporal mask modeling mechanism.Additionally, we present comprehensive studies on how VDT handles conditioning information with the mask modeling mechanism, which we believe will benefit future research and advance the field. Codes and models are available at the https://VDT-2023.github.io. | [] | [] | VDT: General-purpose Video Diffusion Transformers via Mask Modeling | [
"Haoyu Lu",
"Guoxing Yang",
"Nanyi Fei",
"Yuqi Huo",
"Zhiwu Lu",
"Ping Luo",
"Mingyu Ding"
] | 2305.13311 | 18,520 | https://openreview.net/forum?id=Un0rgm9f04 |
|
[] | Poster | [
"https://github.com/nlpxucan/WizardLM"
] | Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated remarkable performance in various code-related tasks. However, different from their counterparts in the general language modeling field, the technique of instruction fine-tuning remains relatively under-researched in this domain. In this paper, we present Code Evol-Instruct, a novel approach that adapts the Evol-Instruct method to the realm of code, enhancing Code LLMs to create novel models WizardCoder. Through comprehensive experiments on five prominent code generation benchmarks, namely HumanEval, HumanEval+, MBPP, DS-1000, and MultiPL-E, our models showcase outstanding performance. They consistently outperform all other open-source Code LLMs by a significant margin. Remarkably, WizardCoder 15B even surpasses the largest closed-source LLMs, including Anthropic’s Claude and Google’s Bard, on the HumanEval and HumanEval+ benchmarks. Additionally, WizardCoder 34B not only achieves a HumanEval score comparable to GPT3.5 (ChatGPT) but also surpasses it on the HumanEval+ benchmark. Furthermore, our preliminary exploration highlights the pivotal role of instruction complexity in achieving exceptional coding performance. | [] | [] | WizardCoder: Empowering Code Large Language Models with Evol-Instruct | [
"Ziyang Luo",
"Can Xu",
"Pu Zhao",
"Qingfeng Sun",
"Xiubo Geng",
"Wenxiang Hu",
"Chongyang Tao",
"Jing Ma",
"Qingwei Lin",
"Daxin Jiang"
] | 2306.08568 | 18,519 | https://openreview.net/forum?id=UnUwSIgK5W |
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[] | Poster | [] | In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity generally has a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights — in particular their effective rank — influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting. | [] | [] | How connectivity structure shapes rich and lazy learning in neural circuits | [
"Yuhan Helena Liu",
"Aristide Baratin",
"Jonathan Cornford",
"Stefan Mihalas",
"Eric Todd SheaBrown",
"Guillaume Lajoie"
] | 2310.08513 | 17,636 | https://openreview.net/forum?id=slSmYGc8ee |
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[] | Poster | [] | Lattices are architected metamaterials whose properties strongly depend on their geometrical design.The analogy between lattices and graphs enables the use of graph neural networks (GNNs) as a faster surrogate model compared to traditional methods such as finite element modelling.In this work we present a higher-order GNN model trained to predict the fourth-order stiffness tensor of periodic strut-based lattices.The key features of the model are (i) SE(3) equivariance, and (ii) consistency with the thermodynamic law of conservation of energy.We compare the model to non-equivariant models based on a number of error metrics and demonstrate the benefits of the encoded equivariance and energy conservation in terms of predictive performance and reduced training requirements. | [] | [] | Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials | [
"Ivan Grega",
"Ilyes Batatia",
"Gabor Csanyi",
"Sri Karlapati",
"Vikram Deshpande"
] | 2401.16914 | 17,634 | https://openreview.net/forum?id=smy4DsUbBo |
|
[] | Spotlight Poster | [] | Recent deep music generation studies have put much emphasis on \textit{music structure} and \textit{long-term} generation. However, we are yet to see high-quality, well-structured whole-song generation. In this paper, we make the first attempt to model a full music piece under the realization of \textit{compositional hierarchy}. With a focus on symbolic representations of pop songs, we define a hierarchical language, in which each level of hierarchy focuses on the context dependency at a certain music scope. The high-level languages reveal whole-song form, phrase, and cadence, whereas the low-level languages focus on notes, chords, and their local patterns. A cascaded diffusion model is trained to model the hierarchical language, where each level is conditioned on its upper levels. Experiments and analysis show that our model is capable of generating full-piece music with recognizable global verse-chorus structure and cadences, and the music quality is higher than the baselines. Additionally, we show that the proposed model is \textit{controllable} in a flexible way. By sampling from the interpretable hierarchical languages or adjusting external model controls, users can control the music flow via various features such as phrase harmonic structures, rhythmic patterns, and accompaniment texture. | [] | [] | Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models | [
"Ziyu Wang",
"Lejun Min",
"Gus Xia"
] | 17,633 | https://openreview.net/forum?id=sn7CYWyavh |
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[] | Spotlight Poster | [] | Cascaded models are multi-scale generative models with a marked capacity for producing perceptually impressive samples at high resolutions. In this work, we show that they can also be excellent likelihood models, so long as we overcome a fundamental difficulty with probabilistic multi-scale models: the intractability of the likelihood function. Chiefly, in cascaded models each intermediary scale introduces extraneous variables that cannot be tractably marginalized out for likelihood evaluation. This issue vanishes by modeling the diffusion process on latent spaces induced by a class of transformations we call hierarchical volume-preserving maps, which decompose spatially structured data in a hierarchical fashion without introducing local distortions in the latent space. We demonstrate that two such maps are well-known in the literature for multiscale modeling: Laplacian pyramids and wavelet transforms. Not only do such reparameterizations allow the likelihood function to be directly expressed as a joint likelihood over the scales, we show that the Laplacian pyramid and wavelet transform also produces significant improvements to the state-of-the-art on a selection of benchmarks in likelihood modeling, including density estimation, lossless compression, and out-of-distribution detection. Investigating the theoretical basis of our empirical gains we uncover deep connections to score matching under the Earth Mover's Distance (EMD), which is a well-known surrogate for perceptual similarity. | [] | [] | Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps | [
"Henry Li",
"Ronen Basri",
"Yuval Kluger"
] | 17,632 | https://openreview.net/forum?id=sojpn00o8z |
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[] | Poster | [] | We introduce a new challenge to test the STEM skills of neural models. Unlike existing datasets, our dataset requires the understanding of multimodal vision-language information. Our dataset features one of the largest and most comprehensive datasets for the challenge. It includes 448 skills and 1,073,146 questions spanning all STEM (science, technology, engineering, math) subjects. Compared to existing datasets that often focus on examining expert-level ability, our dataset includes fundamental skills and questions designed based on the K-12 curriculum. We also add state-of-the-art foundation models such as CLIP and ChatGPT to our dataset. Results show that the recent model advances only help master a very limited number of lower grade-level skills (2.5% in the third grade) in our dataset. In fact, these models are still well below (averaging 54.7%) the performance of elementary students, not to mention near expert-level performance. To understand and increase the performance on our dataset, we teach the models on a training split of our dataset. Even though we observe improved performance, the model performance remains relatively low compared to average elementary students. To solve STEM problems, we will need novel algorithmic innovations from the community. The code and dataset are available at https://anonymous.4open.science/r/STEM-Dataset-ICLR-2024 and will be made publicly available. | [] | [] | Measuring Vision-Language STEM Skills of Neural Models | [
"Jianhao Shen",
"Ye Yuan",
"Srbuhi Mirzoyan",
"Ming Zhang",
"Chenguang Wang"
] | 2402.17205 | 17,631 | https://openreview.net/forum?id=spvaV5LELF |
|
[] | Poster | [
"https://github.com/FLAIR-THU/VFLAIR"
] | Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model parameters. VFL has gained significant attention for its research potential and real-world applications in recent years, but still faces substantial challenges, such as in defending various kinds of data inference and backdoor attacks. Moreover, most of existing VFL projects are industry-facing and not easily used for keeping track of the current research progress. To address this need, we present an extensible and lightweight VFL framework VFLAIR (available at https://github.com/FLAIR-THU/VFLAIR), which supports VFL training with a variety of models, datasets and protocols, along with standardized modules for comprehensive evaluations of attacks and defense strategies. We also benchmark $11$ attacks and $8$ defenses performance under different communication and model partition settings and draw concrete insights and recommendations on the choice of defense strategies for different practical VFL deployment scenarios. | [] | [] | VFLAIR: A Research Library and Benchmark for Vertical Federated Learning | [
"Tianyuan Zou",
"Zixuan GU",
"Yu He",
"Hideaki Takahashi",
"Yang Liu",
"Ya-Qin Zhang"
] | 2310.09827 | 17,630 | https://openreview.net/forum?id=sqRgz88TM3 |
|
[] | Spotlight Poster | [
"https://github.com/microsoft/BatteryML"
] | Battery life prediction has been a critical subject for energy storage field, and the incorporation of machine learning in recent years has substantially accelerated its advancements. However, Battery life prediction presents a high technical barrier as a multidisciplinary issue, posing challenges for researchers in both battery and machine learning fields. Machine learning researchers often lack essential knowledge about batteries, and understanding various battery types and related information requires significant time and effort. For battery researchers, unique models are implemented on specific datasets, and the complexity of these models obstructs their adaptation to individual battery data. To address these challenges, we introduce BatteryML, a one-stop open-source platform that streamlines the process, covering data preprocessing, feature extraction, and the application of both classical and cutting-edge models. This efficient approach enables practical applications for researchers. Currently, unified standards for battery life prediction are lacking, encompassing data format and evaluation criteria for predictions. Through BatteryML, we aim to establish these standards, allowing researchers from diverse fields to contribute to battery research and cultivating a collaborative platform for experts across both disciplines. | [] | [] | BatteryML: An Open-source Platform for Machine Learning on Battery Degradation | [
"Han Zhang",
"Xiaofan Gui",
"Shun Zheng",
"Ziheng Lu",
"Yuqi Li",
"Jiang Bian"
] | 2310.14714 | 17,628 | https://openreview.net/forum?id=sxGugrYhP9 |
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[] | Poster | [] | Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for time series forecasting are often specialized, necessitating distinct designs for different tasks and applications. While pre-trained foundation models have made impressive strides in NLP and CV, their development in time series domains has been constrained by data sparsity. Recent studies have revealed that large language models (LLMs) possess robust pattern recognition and reasoning abilities over complex sequences of tokens. However, the challenge remains in effectively aligning the modalities of time series data and natural language to leverage these capabilities. In this work, we present Time-LLM, a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities. To augment the LLM's ability to reason with time series data, we propose Prompt-as-Prefix (PaP), which enriches the input context and directs the transformation of reprogrammed input patches. The transformed time series patches from the LLM are finally projected to obtain the forecasts. Our comprehensive evaluations demonstrate that \method is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models. Moreover, Time-LLM excels in both few-shot and zero-shot learning scenarios. | [] | [] | Time-LLM: Time Series Forecasting by Reprogramming Large Language Models | [
"Ming Jin",
"Shiyu Wang",
"Lintao Ma",
"Zhixuan Chu",
"James Y. Zhang",
"Xiaoming Shi",
"Pin-Yu Chen",
"Yuxuan Liang",
"Yuan-Fang Li",
"Shirui Pan",
"Qingsong Wen"
] | 18,518 | https://openreview.net/forum?id=Unb5CVPtae |
||
[] | Poster | [] | Many complex robotic manipulation tasks can be decomposed as a sequence of pick and place actions. Training a robotic agent to learn this sequence over many different starting conditions typically requires many iterations or demonstrations, especially in 3D environments. In this work, we propose Fourier Transporter ($\text{FourTran}$) which leverages the two-fold $\mathrm{SE}(d)\times\\mathrm{SE}(d)$ symmetry in the pick-place problem to achieve much higher sample efficiency. $\text{FourTran}$ is an open-loop behavior cloning method trained using expert demonstrations to predict pick-place actions on new environments. $\text{FourTran}$ is constrained to incorporate symmetries of the pick and place actions independently. Our method utilizes a fiber space Fourier transformation that allows for memory-efficient construction. We test our proposed network on the RLbench benchmark and achieve state-of-the-art results across various tasks. | [] | [] | Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D | [
"Haojie Huang",
"Owen Lewis Howell",
"Dian Wang",
"Xupeng Zhu",
"Robert Platt",
"Robin Walters"
] | 2401.12046 | 18,515 | https://openreview.net/forum?id=UulwvAU1W0 |
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[] | Poster | [] | This study aims to prove the emergence of symbolic concepts (or more precisely, sparse primitive inference patterns) in well-trained AI models. Specifically, we prove the following three conditions for the emergence. (i) The high-order derivatives of the model output with respect to the input variables are all zero. (ii) The model can be used on occluded samples, and when the input sample is less occluded, the model will yield higher confidence. (iii) The confidence of the model does not significantly degrade on occluded samples. These conditions are quite common, and we prove that under these conditions, the model will only encode a relatively small number of sparse interactions between input variables. Moreover, we can consider such interactions as symbolic primitive inference patterns encoded by an AI model, because we show that inference scores of the model on an exponentially large number of randomly masked samples can always be well mimicked by numerical effects of just a few interactions. | [] | [] | Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs | [
"Qihan Ren",
"Jiayang Gao",
"Wen Shen",
"Quanshi Zhang"
] | 19,495 | https://openreview.net/forum?id=3pWSL8My6B |
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[] | Poster | [
"https://github.com/tianyu139/meaning-as-trajectories"
] | We propose to extract meaning representations from autoregressive language models by considering the distribution of all possible trajectories extending an input text. This strategy is prompt-free, does not require fine-tuning, and is applicable to any pre-trained autoregressive model. Moreover, unlike vector-based representations, distribution-based representations can also model asymmetric relations (e.g., direction of logical entailment, hypernym/hyponym relations) by using algebraic operations between likelihood functions. These ideas are grounded in distributional perspectives on semantics and are connected to standard constructions in automata theory, but to our knowledge they have not been applied to modern language models. We empirically show that the representations obtained from large models align well with human annotations, outperform other zero-shot and prompt-free methods on semantic similarity tasks, and can be used to solve more complex entailment and containment tasks that standard embeddings cannot handle. Finally, we extend our method to represent data from different modalities (e.g., image and text) using multimodal autoregressive models. | [] | [] | Meaning Representations from Trajectories in Autoregressive Models | [
"Tian Yu Liu",
"Matthew Trager",
"Alessandro Achille",
"Pramuditha Perera",
"Luca Zancato",
"Stefano Soatto"
] | 2310.18348 | 18,513 | https://openreview.net/forum?id=UyGWafcopT |
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[] | Poster | [] | Discovering the underlying relationships among variables from temporal observations has been a longstanding challenge in numerous scientific disciplines, including biology, finance, and climate science. The dynamics of such systems are often best described using continuous-time stochastic processes. Unfortunately, most existing structure learning approaches assume that the underlying process evolves in discrete-time and/or observations occur at regular time intervals. These mismatched assumptions can often lead to incorrect learned structures and models. In this work, we introduce a novel structure learning method, SCOTCH, which combines neural stochastic differential equations (SDE) with variational inference to infer a posterior distribution over possible structures. This continuous-time approach can naturally handle both learning from and predicting observations at arbitrary time points. Theoretically, we establish sufficient conditions for an SDE and SCOTCH to be structurally identifiable, and prove its consistency under infinite data limits. Empirically, we demonstrate that our approach leads to improved structure learning performance on both synthetic and real-world datasets compared to relevant baselines under regular and irregular sampling intervals. | [] | [] | Neural structure learning with stochastic differential equations | [
"Benjie Wang",
"Joel Jennings",
"Wenbo Gong"
] | 2311.03309 | 18,511 | https://openreview.net/forum?id=V1GM9xDvIY |
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[] | Poster | [] | Real-time recurrent learning (RTRL) for sequence-processing recurrent neural networks (RNNs) offers certain conceptual advantages over backpropagation through time (BPTT). RTRL requires neither caching past activations nor truncating context, and enables online learning. However, RTRL's time and space complexity make it impractical. To overcome this problem, most recent work on RTRL focuses on approximation theories, while experiments are often limited to diagnostic settings. Here we explore the practical promise of RTRL in more realistic settings. We study actor-critic methods that combine RTRL and policy gradients, and test them in several subsets of DMLab-30, ProcGen, and Atari-2600 environments. On DMLab memory tasks, our system trained on fewer than 1.2B environmental frames is competitive with or outperforms well-known IMPALA and R2D2 baselines trained on 10B frames. To scale to such challenging tasks, we focus on certain well-known neural architectures with element-wise recurrence, allowing for tractable RTRL without approximation. Importantly, we also discuss rarely addressed limitations of RTRL in real-world applications, such as its complexity in the multi-layer case. | [] | [] | Exploring the Promise and Limits of Real-Time Recurrent Learning | [
"Kazuki Irie",
"Anand Gopalakrishnan",
"Jürgen Schmidhuber"
] | 2305.19044 | 18,510 | https://openreview.net/forum?id=V2cBKtdC3a |
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[] | Poster | [] | Recent advances in depthwise-separable convolutional neural networks (DS-CNNs) have led to novel architectures, that surpass the performance of classical CNNs, by a considerable scalability and accuracy margin. This paper reveals another striking property of DS-CNN architectures: discernible and explainable patterns emerge in their trained depthwise convolutional kernels in all layers. Through an extensive analysis of millions of trained filters, with different sizes and from various models, we employed unsupervised clustering with autoencoders, to categorize these filters. Astonishingly, the patterns converged into a few main clusters, each resembling the difference of Gaussian (DoG) functions, and their first and second-order derivatives. Notably, we classify over 95\% and 90\% of the filters from state-of-the-art ConvNeXtV2 and ConvNeXt models, respectively. This finding is not merely a technological curiosity; it echoes the foundational models neuroscientists have long proposed for the vision systems of mammals. Our results thus deepen our understanding of the emergent properties of trained DS-CNNs and provide a bridge between artificial and biological visual processing systems. More broadly, they pave the way for more interpretable and biologically-inspired neural network designs in the future. | [] | [] | Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels | [
"Zahra Babaiee",
"Peyman Kiasari",
"Daniela Rus",
"Radu Grosu"
] | 2401.14469 | 19,461 | https://openreview.net/forum?id=4VgBjsOC8k |
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[] | Poster | [] | Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered considerable attention in recent times. In the context of Fed-LT, existing works have predominantly centered on addressing the data imbalance issue to enhance the efficacy of the generic global model while neglecting the performance at the local level. In contrast, conventional Personalized Federated Learning (pFL) techniques are primarily devised to optimize personalized local models under the presumption of a balanced global data distribution. This paper introduces an approach termed Federated Local and Generic Model Training in Fed-LT (FedLoGe), which enhances both local and generic model performance through the integration of representation learning and classifier alignment within a neural collapse framework. Our investigation reveals the feasibility of employing a shared backbone as a foundational framework for capturing overarching global trends, while concurrently employing individualized classifiers to encapsulate distinct refinements stemming from each client’s local features. Building upon this discovery, we establish the Static Sparse Equiangular Tight Frame Classifier (SSE-C), inspired by neural collapse principles that naturally prune extraneous noisy features and foster the acquisition of potent data representations. Furthermore, leveraging insights from imbalance neural collapse's classifier norm patterns, we develop Global and Local Adaptive Feature Realignment (GLA-FR) via an auxiliary global classifier and personalized Euclidean norm transfer to align global features with client preferences. Extensive experimental results on CIFAR-10/100-LT, ImageNet, and iNaturalist demonstrate the advantage of our method over state-of-the-art pFL and Fed-LT approaches. | [] | [] | FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data | [
"Zikai Xiao",
"Zihan Chen",
"Liyinglan Liu",
"YANG FENG",
"Joey Tianyi Zhou",
"Jian Wu",
"Wanlu Liu",
"Howard Hao Yang",
"Zuozhu Liu"
] | 2401.08977 | 18,509 | https://openreview.net/forum?id=V3j5d0GQgH |
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[] | Poster | [] | The behavior of neural networks still remains opaque, and a recently widely noted phenomenon is that networks often achieve similar performance when initialized with different random parameters. This phenomenon has attracted significant attention in measuring the similarity between features learned by distinct networks. However, feature similarity could be vague in describing the same feature since equivalent features hardly exist. In this paper, we expand the concept of equivalent feature and provide the definition of what we call *functionally equivalent features*. These features produce equivalent output under certain transformations. Using this definition, we aim to derive a more intrinsic metric for the so-called *feature complexity* regarding the redundancy of features learned by a neural network at each layer. We offer a formal interpretation of our approach through the lens of category theory, a well-developed area in mathematics. To quantify the feature complexity, we further propose an efficient algorithm named Iterative Feature Merging. Our experimental results validate our ideas and theories from various perspectives. We empirically demonstrate that the functionally equivalence widely exists among different features learned by the same neural network and we could reduce the number of parameters of the network without affecting the performance. We have also drawn several interesting empirical findings, including: 1) the larger the network, the more redundant features it learns; 2) in particular, we show how to prune the networks based on our finding using direct equivalent feature merging, without fine-tuning which is often needed in peer network pruning methods; 3) same structured networks with higher feature complexity achieve better performance; 4) through the layers of a neural network, the feature complexity first increase then decrease; 5) for the image classification task, a group of functionally equivalent features may correspond to a specific semantic meaning. Source code will be made publicly available. | [] | [] | Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory | [
"Yiting Chen",
"Zhanpeng Zhou",
"Junchi Yan"
] | 2310.06756 | 19,454 | https://openreview.net/forum?id=4bSQ3lsfEV |
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[] | Poster | [] | Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes. Conformal inference algorithms construct prediction sets guaranteed to contain the true label with high probability. These guarantees fail to hold in the face of distribution shift, which is precisely when reliable uncertainty quantification can be most useful. We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting. It estimates importance weights, then propagates uncertainty in these estimates through a Gaussian elimination algorithm to compute confidence intervals that contain the importance weights, and finally uses these intervals to construct prediction sets. We evaluate our approach on four datasets: the CIFAR-10 and ChestX-Ray image datasets, the tabular CDC Heart Dataset, and the AGNews text dataset. Our algorithm satisfies the PAC guarantee while producing smaller prediction set sizes compared to several baselines. | [] | [] | PAC Prediction Sets Under Label Shift | [
"Wenwen Si",
"Sangdon Park",
"Insup Lee",
"Edgar Dobriban",
"Osbert Bastani"
] | 2310.12964 | 19,445 | https://openreview.net/forum?id=4vPVBh3fhz |
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[] | Poster | [] | Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems. However, they still make unjustified logical and computational errors in their reasoning steps and answers. In this paper, we leverage the fact that if the training corpus of LLMs contained sufficiently many examples of formal mathematics (e.g. in Isabelle, a formal theorem proving environment), they can be prompted to translate i.e. autoformalize informal mathematical statements into formal Isabelle code --- which can be verified automatically for internal consistency. This provides a mechanism to automatically reject solutions whose formalized versions are inconsistent within themselves or with the formalized problem statement. We evaluate our method on GSM8K, MATH and MultiArith datasets and demonstrate that our approach provides a consistently better heuristic than vanilla majority voting --- the previously best method to identify correct answers, by more than 12\% on GSM8K. In our experiments it improves results consistently across all datasets and LLM model sizes. | [] | [] | Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization | [
"Jin Peng Zhou",
"Charles E Staats",
"Wenda Li",
"Christian Szegedy",
"Kilian Q Weinberger",
"Yuhuai Wu"
] | 18,508 | https://openreview.net/forum?id=V5tdi14ple |
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[] | Poster | [] | Numerous explainability methods have been proposed to shed light on the inner workings of GNNs. Despite the inclusion of empirical evaluations in all the proposed algorithms, the interrogative aspects of these evaluations lack diversity. As a result, various facets of explainability pertaining to GNNs, such as a comparative analysis of counterfactual reasoners, their stability to variational factors such as different GNN architectures, noise, stochasticity in non-convex loss surfaces, feasibility amidst domain constraints, and so forth, have yet to be formally investigated. Motivated by this need, we present a benchmarking study on perturbation-based explainability methods for GNNs, aiming to systematically evaluate and compare a wide range of explainability techniques. Among the key findings of our study, we identify the Pareto-optimal methods that exhibit superior efficacy and stability in the presence of noise. Nonetheless, our study reveals thatall algorithms are affected by stability issues when faced with noisy data. Furthermore, we have established that the current generation of counterfactual explainers often fails to provide feasible recourses due to violations of topological constraints encoded by domain-specific considerations. Overall, this benchmarking study empowers stakeholders in the field of GNNs with a comprehensive understanding of the state-of-the-art explainability methods, potential research problems for further enhancement, and the implications of their application in real-world scenarios. | [] | [] | GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking | [
"Mert Kosan",
"Samidha Verma",
"Burouj Armgaan",
"Khushbu Pahwa",
"Ambuj Singh",
"Sourav Medya",
"Sayan Ranu"
] | 18,507 | https://openreview.net/forum?id=VJvbOSXRUq |
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[] | Poster | [] | We introduce Tangent Attention Fine-Tuning (TAFT), a method for fine-tuning linearized transformers obtained by computing a First-order Taylor Expansion around a pre-trained initialization. We show that the Jacobian-Vector Product resulting from linearization can be computed efficiently in a single forward pass, reducing training and inference cost to the same order of magnitude as its original non-linear counterpart, while using the same number of parameters. Furthermore, we show that, when applied to various downstream visual classification tasks, the resulting Tangent Transformer fine-tuned with TAFT can perform comparably with fine-tuning the original non-linear network. Since Tangent Transformers are linear with respect to the new set of weights, and the resulting fine-tuning loss is convex, we show that TAFT enjoys several advantages compared to non-linear fine-tuning when it comes to model composition, parallel training, machine unlearning, and differential privacy. | [] | [] | Tangent Transformers for Composition,Privacy and Removal | [
"Tian Yu Liu",
"Aditya Golatkar",
"Stefano Soatto"
] | 2307.08122 | 18,506 | https://openreview.net/forum?id=VLFhbOCz5D |
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[] | Poster | [] | The task of out-of-distribution (OOD) detection is notoriously ill-defined. Earlier works focused on new-class detection, aiming to identify label-altering data distribution shifts, also known as "semantic shift." However, recent works argue for a focus on failure detection, expanding the OOD evaluation framework to account for label-preserving data distribution shifts, also known as "covariate shift.” Intriguingly, under this new framework, complex OOD detectors that were previously considered state-of-the-art now perform similarly to, or even worse than the simple maximum softmax probability baseline. This raises the question: what are the latest OOD detectors actually detecting? Deciphering the behavior of OOD detection algorithms requires evaluation datasets that decouples semantic shift and covariate shift. To aid our investigations, we present ImageNet-OOD, a clean semantic shift dataset that minimizes the interference of covariate shift. Through comprehensive experiments, we show that OOD detectors are more sensitive to covariate shift than to semantic shift, and the benefits of recent OOD detection algorithms on semantic shift detection is minimal. Our dataset and analyses provide important insights for guiding the design of future OOD detectors. | [] | [] | ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms | [
"William Yang",
"Byron Zhang",
"Olga Russakovsky"
] | 18,504 | https://openreview.net/forum?id=VTYg5ykEGS |
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