abs stringlengths 44 64 | Download PDF stringlengths 75 115 | OpenReview stringlengths 42 42 | title stringlengths 15 148 | url stringlengths 44 64 | authors stringlengths 6 903 | detail_url stringlengths 44 64 | tags stringclasses 1
value | abstract stringlengths 422 5.84k |
|---|---|---|---|---|---|---|---|---|
https://proceedings.mlr.press/v235/cachet24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cachet24a/cachet24a.pdf | https://openreview.net/forum?id=ZrM67ZZ5vj | Bridging Environments and Language with Rendering Functions and Vision-Language Models | https://proceedings.mlr.press/v235/cachet24a.html | Theo Cachet, Christopher R Dance, Olivier Sigaud | https://proceedings.mlr.press/v235/cachet24a.html | ICML 2024 | Vision-language models (VLMs) have tremendous potential for grounding language, and thus enabling language-conditioned agents (LCAs) to perform diverse tasks specified with text. This has motivated the study of LCAs based on reinforcement learning (RL) with rewards given by rendering images of an environment and evalua... |
https://proceedings.mlr.press/v235/cai24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cai24a/cai24a.pdf | https://openreview.net/forum?id=PnyYgWMMwj | Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions | https://proceedings.mlr.press/v235/cai24a.html | Yongqiang Cai | https://proceedings.mlr.press/v235/cai24a.html | ICML 2024 | In recent years, deep learning-based sequence modelings, such as language models, have received much attention and success, which pushes researchers to explore the possibility of transforming non-sequential problems into a sequential form. Following this thought, deep neural networks can be represented as composite fun... |
https://proceedings.mlr.press/v235/cai24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cai24b/cai24b.pdf | https://openreview.net/forum?id=PEpbUobfJv | Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads | https://proceedings.mlr.press/v235/cai24b.html | Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, Tri Dao | https://proceedings.mlr.press/v235/cai24b.html | ICML 2024 | Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one’s output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator’s cache. While methods such as ... |
https://proceedings.mlr.press/v235/cai24c.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cai24c/cai24c.pdf | https://openreview.net/forum?id=YlcSyCz21c | Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation | https://proceedings.mlr.press/v235/cai24c.html | Lincan Cai, Shuang Li, Wenxuan Ma, Jingxuan Kang, Binhui Xie, Zixun Sun, Chengwei Zhu | https://proceedings.mlr.press/v235/cai24c.html | ICML 2024 | Large-scale pretrained models have proven immensely valuable in handling data-intensive modalities like text and image. However, fine-tuning these models for certain specialized modalities, such as protein sequence and cosmic ray, poses challenges due to the significant modality discrepancy and scarcity of labeled data... |
https://proceedings.mlr.press/v235/cai24d.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cai24d/cai24d.pdf | https://openreview.net/forum?id=bplNmU2ROC | Batch and match: black-box variational inference with a score-based divergence | https://proceedings.mlr.press/v235/cai24d.html | Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David Blei, Lawrence K. Saul | https://proceedings.mlr.press/v235/cai24d.html | ICML 2024 | Most leading implementations of black-box variational inference (BBVI) are based on optimizing a stochastic evidence lower bound (ELBO). But such approaches to BBVI often converge slowly due to the high variance of their gradient estimates and their sensitivity to hyperparameters. In this work, we propose batch and mat... |
https://proceedings.mlr.press/v235/cai24e.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cai24e/cai24e.pdf | https://openreview.net/forum?id=9vKRhnflAs | Flextron: Many-in-One Flexible Large Language Model | https://proceedings.mlr.press/v235/cai24e.html | Ruisi Cai, Saurav Muralidharan, Greg Heinrich, Hongxu Yin, Zhangyang Wang, Jan Kautz, Pavlo Molchanov | https://proceedings.mlr.press/v235/cai24e.html | ICML 2024 | Training modern LLMs is extremely resource intensive, and customizing them for various deployment scenarios characterized by limited compute and memory resources through repeated training is impractical. In this paper, we introduce Flextron, a network architecture and post-training model optimization framework supporti... |
https://proceedings.mlr.press/v235/cai24f.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cai24f/cai24f.pdf | https://openreview.net/forum?id=EK7fuAMNoI | Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion | https://proceedings.mlr.press/v235/cai24f.html | Yang Cai, Argyris Oikonomou, Weiqiang Zheng | https://proceedings.mlr.press/v235/cai24f.html | ICML 2024 | We study constrained comonotone min-max optimization, a structured class of nonconvex-nonconcave min-max optimization problems, and their generalization to comonotone inclusion. In our first contribution, we extend the Extra Anchored Gradient (EAG) algorithm, originally proposed by Yoon and Ryu (2021) for unconstrained... |
https://proceedings.mlr.press/v235/cai24g.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cai24g/cai24g.pdf | https://openreview.net/forum?id=NUlyqMyhO9 | LoCoCo: Dropping In Convolutions for Long Context Compression | https://proceedings.mlr.press/v235/cai24g.html | Ruisi Cai, Yuandong Tian, Zhangyang Wang, Beidi Chen | https://proceedings.mlr.press/v235/cai24g.html | ICML 2024 | This paper tackles the memory hurdle of of processing long context sequences in Large Language Models (LLMs), by presenting a novel approach, Dropping In Convolutions for Long Context Compression (LoCoCo). LoCoCo employs only a fixed-size Key-Value (KV) cache, and can enhance efficiency in both inference and fine-tunin... |
https://proceedings.mlr.press/v235/cai24h.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cai24h/cai24h.pdf | https://openreview.net/forum?id=YB1O99gK7b | On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box | https://proceedings.mlr.press/v235/cai24h.html | Yi Cai, Gerhard Wunder | https://proceedings.mlr.press/v235/cai24h.html | ICML 2024 | Attribution methods shed light on the explainability of data-driven approaches such as deep learning models by uncovering the most influential features in a to-be-explained decision. While determining feature attributions via gradients delivers promising results, the internal access required for acquiring gradients can... |
https://proceedings.mlr.press/v235/cai24i.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cai24i/cai24i.pdf | https://openreview.net/forum?id=4sikyurTLX | Sample-specific Masks for Visual Reprogramming-based Prompting | https://proceedings.mlr.press/v235/cai24i.html | Chengyi Cai, Zesheng Ye, Lei Feng, Jianzhong Qi, Feng Liu | https://proceedings.mlr.press/v235/cai24i.html | ICML 2024 | Visual reprogramming (VR) is a prompting technique that aims to re-purpose a pre-trained model (e.g., a classifier on ImageNet) to target tasks (e.g., medical data prediction) by learning a small-scale pattern added into input images instead of tuning considerable parameters within the model. The location of the patter... |
https://proceedings.mlr.press/v235/calandriello24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/calandriello24a/calandriello24a.pdf | https://openreview.net/forum?id=2RQqg2Y7Y6 | Human Alignment of Large Language Models through Online Preference Optimisation | https://proceedings.mlr.press/v235/calandriello24a.html | Daniele Calandriello, Zhaohan Daniel Guo, Remi Munos, Mark Rowland, Yunhao Tang, Bernardo Avila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot | https://proceedings.mlr.press/v235/calandriello24a.html | ICML 2024 | Ensuring alignment of language model’s outputs with human preferences is critical to guarantee a useful, safe, and pleasant user experience. Thus, human alignment has been extensively studied recently and several methods such as Reinforcement Learning from Human Feedback (RLHF), Direct Policy Optimisation (DPO) and Seq... |
https://proceedings.mlr.press/v235/calvo-ordonez24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/calvo-ordonez24a/calvo-ordonez24a.pdf | https://openreview.net/forum?id=jNab9mXEyj | Partially Stochastic Infinitely Deep Bayesian Neural Networks | https://proceedings.mlr.press/v235/calvo-ordonez24a.html | Sergio Calvo Ordoñez, Matthieu Meunier, Francesco Piatti, Yuantao Shi | https://proceedings.mlr.press/v235/calvo-ordonez24a.html | ICML 2024 | In this paper, we present Partially Stochastic Infinitely Deep Bayesian Neural Networks, a novel family of architectures that integrates partial stochasticity into the framework of infinitely deep neural networks. Our new class of architectures is designed to improve the computational efficiency of existing architectur... |
https://proceedings.mlr.press/v235/campbell24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/campbell24a/campbell24a.pdf | https://openreview.net/forum?id=kQwSbv0BR4 | Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design | https://proceedings.mlr.press/v235/campbell24a.html | Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi Jaakkola | https://proceedings.mlr.press/v235/campbell24a.html | ICML 2024 | Combining discrete and continuous data is an important capability for generative models. We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that provides the missing link in enabling flow-based generative models to be applied to multimodal continuous and discrete data problems. Our key insi... |
https://proceedings.mlr.press/v235/candido-ramos24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/candido-ramos24a/candido-ramos24a.pdf | https://openreview.net/forum?id=JAfIDm7NED | Mimicking Better by Matching the Approximate Action Distribution | https://proceedings.mlr.press/v235/candido-ramos24a.html | Joao Candido Ramos, Lionel Blondé, Naoya Takeishi, Alexandros Kalousis | https://proceedings.mlr.press/v235/candido-ramos24a.html | ICML 2024 | In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. MAAD utilizes a surrogate reward signal, which can be derived from various sources such as adversarial games, trajectory matching objectives, or optimal transport criteria. To compensate for the non... |
https://proceedings.mlr.press/v235/canturk24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/canturk24a/canturk24a.pdf | https://openreview.net/forum?id=UTSCK582Yo | Graph Positional and Structural Encoder | https://proceedings.mlr.press/v235/canturk24a.html | Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné, Vincent Létourneau, Guy Wolf, Dominique Beaini, Ladislav Rampášek | https://proceedings.mlr.press/v235/canturk24a.html | ICML 2024 | Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, rendering them essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for all graph prediction tasks is a challenging and unsolved problem. Here, we pre... |
https://proceedings.mlr.press/v235/cao24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cao24a/cao24a.pdf | https://openreview.net/forum?id=LJcIIhqGDN | Successor Features for Efficient Multi-Subject Controlled Text Generation | https://proceedings.mlr.press/v235/cao24a.html | Meng Cao, Mehdi Fatemi, Jackie Ck Cheung, Samira Shabanian | https://proceedings.mlr.press/v235/cao24a.html | ICML 2024 | While large language models (LLMs) have achieved impressive performance in generating fluent and realistic text, controlling the generated text so that it exhibits properties such as safety, factuality, and non-toxicity remains challenging. Existing decoding-based controllable text generation methods are static in term... |
https://proceedings.mlr.press/v235/cao24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cao24b/cao24b.pdf | https://openreview.net/forum?id=PAbkWU0KDG | Limited Preference Aided Imitation Learning from Imperfect Demonstrations | https://proceedings.mlr.press/v235/cao24b.html | Xingchen Cao, Fan-Ming Luo, Junyin Ye, Tian Xu, Zhilong Zhang, Yang Yu | https://proceedings.mlr.press/v235/cao24b.html | ICML 2024 | Imitation learning mimics high-quality policies from expert data for sequential decision-making tasks. However, its efficacy is hindered in scenarios where optimal demonstrations are unavailable, and only imperfect demonstrations are present. To address this issue, introducing additional limited human preferences is a ... |
https://proceedings.mlr.press/v235/cao24c.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cao24c/cao24c.pdf | https://openreview.net/forum?id=LYpGLrC4oq | Predictive Dynamic Fusion | https://proceedings.mlr.press/v235/cao24c.html | Bing Cao, Yinan Xia, Yi Ding, Changqing Zhang, Qinghua Hu | https://proceedings.mlr.press/v235/cao24c.html | ICML 2024 | Multimodal fusion is crucial in joint decision-making systems for rendering holistic judgments. Since multimodal data changes in open environments, dynamic fusion has emerged and achieved remarkable progress in numerous applications. However, most existing dynamic multimodal fusion methods lack theoretical guarantees a... |
https://proceedings.mlr.press/v235/cao24d.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cao24d/cao24d.pdf | https://openreview.net/forum?id=xZO7SmM12y | Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection | https://proceedings.mlr.press/v235/cao24d.html | Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han | https://proceedings.mlr.press/v235/cao24d.html | ICML 2024 | Detecting out-of-distribution (OOD) samples is essential when deploying machine learning models in open-world scenarios. Zero-shot OOD detection, requiring no training on in-distribution (ID) data, has been possible with the advent of vision-language models like CLIP. Existing methods build a text-based classifier with... |
https://proceedings.mlr.press/v235/caragiannis24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/caragiannis24a/caragiannis24a.pdf | https://openreview.net/forum?id=nsjfoziR5j | Can a Few Decide for Many? The Metric Distortion of Sortition | https://proceedings.mlr.press/v235/caragiannis24a.html | Ioannis Caragiannis, Evi Micha, Jannik Peters | https://proceedings.mlr.press/v235/caragiannis24a.html | ICML 2024 | Recent works have studied the design of algorithms for selecting representative sortition panels. However, the most central question remains unaddressed: Do these panels reflect the entire population’s opinion? We present a positive answer by adopting the concept of metric distortion from computational social choice, w... |
https://proceedings.mlr.press/v235/carlini24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/carlini24a/carlini24a.pdf | https://openreview.net/forum?id=VE3yWXt3KB | Stealing part of a production language model | https://proceedings.mlr.press/v235/carlini24a.html | Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr | https://proceedings.mlr.press/v235/carlini24a.html | ICML 2024 | We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like OpenAI’s ChatGPT or Google’s PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under $... |
https://proceedings.mlr.press/v235/carroll24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/carroll24a/carroll24a.pdf | https://openreview.net/forum?id=itYGbe0Cs1 | AI Alignment with Changing and Influenceable Reward Functions | https://proceedings.mlr.press/v235/carroll24a.html | Micah Carroll, Davis Foote, Anand Siththaranjan, Stuart Russell, Anca Dragan | https://proceedings.mlr.press/v235/carroll24a.html | ICML 2024 | Existing AI alignment approaches assume that preferences are static, which is unrealistic: our preferences change, and may even be influenced by our interactions with AI systems themselves. To clarify the consequences of incorrectly assuming static preferences, we introduce Dynamic Reward Markov Decision Processes (DR-... |
https://proceedings.mlr.press/v235/cassel24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cassel24a/cassel24a.pdf | https://openreview.net/forum?id=hXQOO6VsxH | Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback | https://proceedings.mlr.press/v235/cassel24a.html | Asaf Cassel, Haipeng Luo, Aviv Rosenberg, Dmitry Sotnikov | https://proceedings.mlr.press/v235/cassel24a.html | ICML 2024 | In many real-world applications, it is hard to provide a reward signal in each step of a Reinforcement Learning (RL) process and more natural to give feedback when an episode ends. To this end, we study the recently proposed model of RL with Aggregate Bandit Feedback (RL-ABF), where the agent only observes the sum of r... |
https://proceedings.mlr.press/v235/castiglioni24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/castiglioni24a/castiglioni24a.pdf | https://openreview.net/forum?id=shzEkKPrsn | Online Learning under Budget and ROI Constraints via Weak Adaptivity | https://proceedings.mlr.press/v235/castiglioni24a.html | Matteo Castiglioni, Andrea Celli, Christian Kroer | https://proceedings.mlr.press/v235/castiglioni24a.html | ICML 2024 | We study online learning problems in which a decision maker has to make a sequence of costly decisions, with the goal of maximizing their expected reward while adhering to budget and return-on-investment (ROI) constraints. Existing primal-dual algorithms designed for constrained online learning problems under adversari... |
https://proceedings.mlr.press/v235/castin24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/castin24a/castin24a.pdf | https://openreview.net/forum?id=aP0H8A1ywk | How Smooth Is Attention? | https://proceedings.mlr.press/v235/castin24a.html | Valérie Castin, Pierre Ablin, Gabriel Peyré | https://proceedings.mlr.press/v235/castin24a.html | ICML 2024 | Self-attention and masked self-attention are at the heart of Transformers’ outstanding success. Still, our mathematical understanding of attention, in particular of its Lipschitz properties — which are key when it comes to analyzing robustness and expressive power — is incomplete. We provide a detailed study of the Lip... |
https://proceedings.mlr.press/v235/catalano24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/catalano24a/catalano24a.pdf | https://openreview.net/forum?id=cmy38XZlJu | Hierarchical Integral Probability Metrics: A distance on random probability measures with low sample complexity | https://proceedings.mlr.press/v235/catalano24a.html | Marta Catalano, Hugo Lavenant | https://proceedings.mlr.press/v235/catalano24a.html | ICML 2024 | Random probabilities are a key component to many nonparametric methods in Statistics and Machine Learning. To quantify comparisons between different laws of random probabilities several works are starting to use the elegant Wasserstein over Wasserstein distance. In this paper we prove that the infinite dimensionality o... |
https://proceedings.mlr.press/v235/cattaneo24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cattaneo24a/cattaneo24a.pdf | https://openreview.net/forum?id=y8YovS0lOg | On the Implicit Bias of Adam | https://proceedings.mlr.press/v235/cattaneo24a.html | Matias D. Cattaneo, Jason Matthew Klusowski, Boris Shigida | https://proceedings.mlr.press/v235/cattaneo24a.html | ICML 2024 | In previous literature, backward error analysis was used to find ordinary differential equations (ODEs) approximating the gradient descent trajectory. It was found that finite step sizes implicitly regularize solutions because terms appearing in the ODEs penalize the two-norm of the loss gradients. We prove that the ex... |
https://proceedings.mlr.press/v235/celik24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/celik24a/celik24a.pdf | https://openreview.net/forum?id=9ZkUFSwlUH | Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts | https://proceedings.mlr.press/v235/celik24a.html | Onur Celik, Aleksandar Taranovic, Gerhard Neumann | https://proceedings.mlr.press/v235/celik24a.html | ICML 2024 | Reinforcement learning (RL) is a powerful approach for acquiring a good-performing policy. However, learning diverse skills is challenging in RL due to the commonly used Gaussian policy parameterization. We propose Diverse Skill Learning (Di-SkilL), an RL method for learning diverse skills using Mixture of Experts, whe... |
https://proceedings.mlr.press/v235/celis24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/celis24a/celis24a.pdf | https://openreview.net/forum?id=9QRcp2ubDt | Centralized Selection with Preferences in the Presence of Biases | https://proceedings.mlr.press/v235/celis24a.html | L. Elisa Celis, Amit Kumar, Nisheeth K. Vishnoi, Andrew Xu | https://proceedings.mlr.press/v235/celis24a.html | ICML 2024 | This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the institutions, and the goal is to select candidates for each institution in... |
https://proceedings.mlr.press/v235/cen24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cen24a/cen24a.pdf | https://openreview.net/forum?id=o1gS6MNAw8 | Using Left and Right Brains Together: Towards Vision and Language Planning | https://proceedings.mlr.press/v235/cen24a.html | Jun Cen, Chenfei Wu, Xiao Liu, Shengming Yin, Yixuan Pei, Jinglong Yang, Qifeng Chen, Nan Duan, Jianguo Zhang | https://proceedings.mlr.press/v235/cen24a.html | ICML 2024 | Large Language Models (LLMs) and Large Multi-modality Models (LMMs) have demonstrated remarkable decision masking capabilities on a variety of tasks. However, they inherently operate planning within the language space, lacking the vision and spatial imagination ability. In contrast, humans utilize both left and right h... |
https://proceedings.mlr.press/v235/cen24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cen24b/cen24b.pdf | https://openreview.net/forum?id=JNHK11bAGl | Feasibility Consistent Representation Learning for Safe Reinforcement Learning | https://proceedings.mlr.press/v235/cen24b.html | Zhepeng Cen, Yihang Yao, Zuxin Liu, Ding Zhao | https://proceedings.mlr.press/v235/cen24b.html | ICML 2024 | In the field of safe reinforcement learning (RL), finding a balance between satisfying safety constraints and optimizing reward performance presents a significant challenge. A key obstacle in this endeavor is the estimation of safety constraints, which is typically more difficult than estimating a reward metric due to ... |
https://proceedings.mlr.press/v235/cetin24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cetin24a/cetin24a.pdf | https://openreview.net/forum?id=japBn31gXC | Simple Ingredients for Offline Reinforcement Learning | https://proceedings.mlr.press/v235/cetin24a.html | Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati | https://proceedings.mlr.press/v235/cetin24a.html | ICML 2024 | Offline reinforcement learning algorithms have proven effective on datasets highly connected to the target downstream task. Yet, by leveraging a novel testbed (MOOD) in which trajectories come from heterogeneous sources, we show that existing methods struggle with diverse data: their performance considerably deteriorat... |
https://proceedings.mlr.press/v235/cha24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/cha24a/cha24a.pdf | https://openreview.net/forum?id=9jXS07TIBH | Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning | https://proceedings.mlr.press/v235/cha24a.html | Sungmin Cha, Kyunghyun Cho, Taesup Moon | https://proceedings.mlr.press/v235/cha24a.html | ICML 2024 | We introduce a novel Pseudo-Negative Regularization (PNR) framework for effective continual self-supervised learning (CSSL). Our PNR leverages pseudo-negatives obtained through model-based augmentation in a way that newly learned representations may not contradict what has been learned in the past. Specifically, for th... |
https://proceedings.mlr.press/v235/chadha24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chadha24a/chadha24a.pdf | https://openreview.net/forum?id=FVmqX0sYz9 | Auditing Private Prediction | https://proceedings.mlr.press/v235/chadha24a.html | Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr | https://proceedings.mlr.press/v235/chadha24a.html | ICML 2024 | Differential privacy (DP) offers a theoretical upper bound on the potential privacy leakage of an algorithm, while empirical auditing establishes a practical lower bound. Auditing techniques exist for DP training algorithms. However machine learning can also be made private at inference. We propose the first framework ... |
https://proceedings.mlr.press/v235/chakraborty24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chakraborty24a/chakraborty24a.pdf | https://openreview.net/forum?id=CJbhtpcyGL | Position: On the Possibilities of AI-Generated Text Detection | https://proceedings.mlr.press/v235/chakraborty24a.html | Souradip Chakraborty, Amrit Bedi, Sicheng Zhu, Bang An, Dinesh Manocha, Furong Huang | https://proceedings.mlr.press/v235/chakraborty24a.html | ICML 2024 | Our study addresses the challenge of distinguishing human-written text from Large Language Model (LLM) outputs. We provide evidence that this differentiation is consistently feasible, except when human and machine text distributions are indistinguishable across their entire support. Employing information theory, we sho... |
https://proceedings.mlr.press/v235/chakraborty24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chakraborty24b/chakraborty24b.pdf | https://openreview.net/forum?id=8tzjEMF0Vq | MaxMin-RLHF: Alignment with Diverse Human Preferences | https://proceedings.mlr.press/v235/chakraborty24b.html | Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Dinesh Manocha, Furong Huang, Amrit Bedi, Mengdi Wang | https://proceedings.mlr.press/v235/chakraborty24b.html | ICML 2024 | Reinforcement Learning from Human Feedback (RLHF) aligns language models to human preferences by employing a singular reward model derived from preference data. However, the single reward model overlooks the rich diversity of human preferences inherent in data collected from multiple users. In this work, we first deriv... |
https://proceedings.mlr.press/v235/chan24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chan24a/chan24a.pdf | https://openreview.net/forum?id=eyxVRMrZ4m | Dense Reward for Free in Reinforcement Learning from Human Feedback | https://proceedings.mlr.press/v235/chan24a.html | Alex James Chan, Hao Sun, Samuel Holt, Mihaela Van Der Schaar | https://proceedings.mlr.press/v235/chan24a.html | ICML 2024 | Reinforcement Learning from Human Feedback (RLHF) has been credited as the key advance that has allowed Large Language Models (LLMs) to effectively follow instructions and produce useful assistance. Classically, this involves generating completions from the LLM in response to a query before using a separate reward mode... |
https://proceedings.mlr.press/v235/chan24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chan24b/chan24b.pdf | https://openreview.net/forum?id=Q8uJyOwOsd | Scribble-Supervised Semantic Segmentation with Prototype-based Feature Augmentation | https://proceedings.mlr.press/v235/chan24b.html | Guiyang Chan, Pengcheng Zhang, Hai Dong, Shunhui Ji, Bainian Chen | https://proceedings.mlr.press/v235/chan24b.html | ICML 2024 | Scribble-supervised semantic segmentation presents a cost-effective training method that utilizes annotations generated through scribbling. It is valued in attaining high performance while minimizing annotation costs, which has made it highly regarded among researchers. Scribble supervision propagates information from ... |
https://proceedings.mlr.press/v235/chang24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chang24a/chang24a.pdf | https://openreview.net/forum?id=fywWm06IGn | Feature Importance Disparities for Data Bias Investigations | https://proceedings.mlr.press/v235/chang24a.html | Peter W Chang, Leor Fishman, Seth Neel | https://proceedings.mlr.press/v235/chang24a.html | ICML 2024 | It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection process, or even conducting real-world experiments to a... |
https://proceedings.mlr.press/v235/chang24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chang24b/chang24b.pdf | https://openreview.net/forum?id=KYrAZSbEv6 | Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting | https://proceedings.mlr.press/v235/chang24b.html | Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander | https://proceedings.mlr.press/v235/chang24b.html | ICML 2024 | A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i.e., row and column sums). Prior approaches to this problem have repurposed the classic iterative proportional fitting (IPF) procedure, a... |
https://proceedings.mlr.press/v235/chang24c.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chang24c/chang24c.pdf | https://openreview.net/forum?id=MjGCD8wk1k | LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits | https://proceedings.mlr.press/v235/chang24c.html | Chen-Chia Chang, Yikang Shen, Shaoze Fan, Jing Li, Shun Zhang, Ningyuan Cao, Yiran Chen, Xin Zhang | https://proceedings.mlr.press/v235/chang24c.html | ICML 2024 | In the realm of electronic and electrical engineering, automation of analog circuit is increasingly vital given the complexity and customized requirements of modern applications. However, existing methods only develop search-based algorithms that require many simulation iterations to design a custom circuit topology, w... |
https://proceedings.mlr.press/v235/chang24d.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chang24d/chang24d.pdf | https://openreview.net/forum?id=jVXJdGQ4eD | MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion | https://proceedings.mlr.press/v235/chang24d.html | Di Chang, Yichun Shi, Quankai Gao, Hongyi Xu, Jessica Fu, Guoxian Song, Qing Yan, Yizhe Zhu, Xiao Yang, Mohammad Soleymani | https://proceedings.mlr.press/v235/chang24d.html | ICML 2024 | In this work, we propose MagicPose, a diffusion-based model for 2D human pose and facial expression retargeting. Specifically, given a reference image, we aim to generate a person’s new images by controlling the poses and facial expressions while keeping the identity unchanged. To this end, we propose a two-stage train... |
https://proceedings.mlr.press/v235/chang24e.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chang24e/chang24e.pdf | https://openreview.net/forum?id=hTiNFCNxM1 | From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions | https://proceedings.mlr.press/v235/chang24e.html | Trenton Chang, Jenna Wiens | https://proceedings.mlr.press/v235/chang24e.html | ICML 2024 | Selective labels occur when label observations are subject to a decision-making process; e.g., diagnoses that depend on the administration of laboratory tests. We study a clinically-inspired selective label problem called disparate censorship, where labeling biases vary across subgroups and unlabeled individuals are im... |
https://proceedings.mlr.press/v235/chanpuriya24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chanpuriya24a/chanpuriya24a.pdf | https://openreview.net/forum?id=0XDO74NlOd | On the Role of Edge Dependency in Graph Generative Models | https://proceedings.mlr.press/v235/chanpuriya24a.html | Sudhanshu Chanpuriya, Cameron N Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis | https://proceedings.mlr.press/v235/chanpuriya24a.html | ICML 2024 | We investigate the trade-off between the representation power of graph generative models and model overlap, i.e., the degree to which the model generates diverse outputs versus regurgitating its training data. In particular, we delineate a nested hierarchy of graph generative models categorized into three levels of com... |
https://proceedings.mlr.press/v235/chattopadhyay24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chattopadhyay24a/chattopadhyay24a.pdf | https://openreview.net/forum?id=yTXv8KDD1P | Performance Bounds for Active Binary Testing with Information Maximization | https://proceedings.mlr.press/v235/chattopadhyay24a.html | Aditya Chattopadhyay, Benjamin David Haeffele, Rene Vidal, Donald Geman | https://proceedings.mlr.press/v235/chattopadhyay24a.html | ICML 2024 | In many applications like experimental design, group testing, and medical diagnosis, the state of a random variable $Y$ is revealed by successively observing the outcomes of binary tests about $Y$. New tests are selected adaptively based on the history of outcomes observed so far. If the number of states of $Y$ is fini... |
https://proceedings.mlr.press/v235/che24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/che24a/che24a.pdf | https://openreview.net/forum?id=R6GT1UDcOW | Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation | https://proceedings.mlr.press/v235/che24a.html | Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A Ramirez, Christopher K Harris, A. Rupam Mahmood, Dale Schuurmans | https://proceedings.mlr.press/v235/che24a.html | ICML 2024 | We prove that the combination of a target network and over-parameterized linear function approximation establishes a weaker convergence condition for bootstrapped value estimation in certain cases, even with off-policy data. Our condition is naturally satisfied for expected updates over the entire state-action space or... |
https://proceedings.mlr.press/v235/chen24a.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24a/chen24a.pdf | https://openreview.net/forum?id=17ZwoHl65h | PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer | https://proceedings.mlr.press/v235/chen24a.html | Chang Chen, Junyeob Baek, Fei Deng, Kenji Kawaguchi, Caglar Gulcehre, Sungjin Ahn | https://proceedings.mlr.press/v235/chen24a.html | ICML 2024 | Despite the recent advancements in offline RL, no unified algorithm could achieve superior performance across a broad range of tasks. Offline value function learning, in particular, struggles with sparse-reward, long-horizon tasks due to the difficulty of solving credit assignment and extrapolation errors that accumula... |
https://proceedings.mlr.press/v235/chen24b.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24b/chen24b.pdf | https://openreview.net/forum?id=F3G2udCF3Q | How Interpretable Are Interpretable Graph Neural Networks? | https://proceedings.mlr.press/v235/chen24b.html | Yongqiang Chen, Yatao Bian, Bo Han, James Cheng | https://proceedings.mlr.press/v235/chen24b.html | ICML 2024 | Interpretable graph neural networks (XGNNs ) are widely adopted in various scientific applications involving graph-structured data. Existing XGNNs predominantly adopt the attention-based mechanism to learn edge or node importance for extracting and making predictions with the interpretable subgraph. However, the repres... |
https://proceedings.mlr.press/v235/chen24c.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24c/chen24c.pdf | https://openreview.net/forum?id=5lI9wm4dws | Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning | https://proceedings.mlr.press/v235/chen24c.html | Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao | https://proceedings.mlr.press/v235/chen24c.html | ICML 2024 | Causal effect estimation under networked interference is an important but challenging problem. Available parametric methods are limited in their model space, while previous semiparametric methods, e.g., leveraging neural networks to fit only one single nuisance function, may still encounter misspecification problems un... |
https://proceedings.mlr.press/v235/chen24d.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24d/chen24d.pdf | https://openreview.net/forum?id=J6prHJsIlf | Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation | https://proceedings.mlr.press/v235/chen24d.html | Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, José Miguel Hernández-Lobato | https://proceedings.mlr.press/v235/chen24d.html | ICML 2024 | We investigate the problem of explainability for machine learning models, focusing on Feature Attribution Methods (FAMs) that evaluate feature importance through perturbation tests. Despite their utility, FAMs struggle to distinguish the contributions of different features, when their prediction changes are similar aft... |
https://proceedings.mlr.press/v235/chen24e.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24e/chen24e.pdf | https://openreview.net/forum?id=rADFNrIss3 | InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models | https://proceedings.mlr.press/v235/chen24e.html | Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou | https://proceedings.mlr.press/v235/chen24e.html | ICML 2024 | Large language models (LLMs) are instruction followers but the performance varies under different instructions. It is challenging to create the best instruction, especially for black-box LLMs on which backpropagation is forbidden. Instead of directly optimizing the discrete instruction, we optimize a low-dimensional so... |
https://proceedings.mlr.press/v235/chen24f.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24f/chen24f.pdf | https://openreview.net/forum?id=61RlaY9EIn | MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective | https://proceedings.mlr.press/v235/chen24f.html | Yizhuo Chen, Chun-Fu Chen, Hsiang Hsu, Shaohan Hu, Marco Pistoia, Tarek F. Abdelzaher | https://proceedings.mlr.press/v235/chen24f.html | ICML 2024 | The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies. The massive collection and usage of data, however, pose an increasing risk for people’s private and sensitive information due to either inadvertent mishandling or malicious ... |
https://proceedings.mlr.press/v235/chen24g.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24g/chen24g.pdf | https://openreview.net/forum?id=g9mYBdooPA | Policy-conditioned Environment Models are More Generalizable | https://proceedings.mlr.press/v235/chen24g.html | Ruifeng Chen, Xiong-Hui Chen, Yihao Sun, Siyuan Xiao, Minhui Li, Yang Yu | https://proceedings.mlr.press/v235/chen24g.html | ICML 2024 | In reinforcement learning, it is crucial to have an accurate environment dynamics model to evaluate different policies’ value in downstream tasks like offline policy optimization and policy evaluation. However, the learned model is known to be inaccurate in predictions when evaluating target policies different from dat... |
https://proceedings.mlr.press/v235/chen24h.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24h/chen24h.pdf | https://openreview.net/forum?id=dbFEFHAD79 | MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark | https://proceedings.mlr.press/v235/chen24h.html | Dongping Chen, Ruoxi Chen, Shilin Zhang, Yaochen Wang, Yinuo Liu, Huichi Zhou, Qihui Zhang, Yao Wan, Pan Zhou, Lichao Sun | https://proceedings.mlr.press/v235/chen24h.html | ICML 2024 | Multimodal Large Language Models (MLLMs) have gained significant attention recently, showing remarkable potential in artificial general intelligence. However, assessing the utility of MLLMs presents considerable challenges, primarily due to the absence multimodal benchmarks that align with human preferences. Drawing in... |
https://proceedings.mlr.press/v235/chen24i.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24i/chen24i.pdf | https://openreview.net/forum?id=4zAHgkiCQg | Premise Order Matters in Reasoning with Large Language Models | https://proceedings.mlr.press/v235/chen24i.html | Xinyun Chen, Ryan Andrew Chi, Xuezhi Wang, Denny Zhou | https://proceedings.mlr.press/v235/chen24i.html | ICML 2024 | Large language models (LLMs) have accomplished remarkable reasoning performance in various domains. However, in the domain of reasoning tasks, we discover a frailty: LLMs are surprisingly brittle to the ordering of the premises, despite the fact that such ordering does not alter the underlying task. In particular, we o... |
https://proceedings.mlr.press/v235/chen24j.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24j/chen24j.pdf | https://openreview.net/forum?id=O4cHTxW9BS | Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models | https://proceedings.mlr.press/v235/chen24j.html | Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu | https://proceedings.mlr.press/v235/chen24j.html | ICML 2024 | Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the prospect of growing a strong LLM out of a weak one without the need for acquiring additional human-annotated data. We propose a new fine-tuning method... |
https://proceedings.mlr.press/v235/chen24k.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24k/chen24k.pdf | https://openreview.net/forum?id=xaSpuvNYwS | Robust Classification via a Single Diffusion Model | https://proceedings.mlr.press/v235/chen24k.html | Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, Jun Zhu | https://proceedings.mlr.press/v235/chen24k.html | ICML 2024 | Diffusion models have been applied to improve adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training. However, diffusion-based purification can be evaded by stronger adaptive attacks while adversarial training does not perform well under uns... |
https://proceedings.mlr.press/v235/chen24l.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24l/chen24l.pdf | https://openreview.net/forum?id=puSMYmHmJW | Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective | https://proceedings.mlr.press/v235/chen24l.html | Yang Chen, Cong Fang, Zhouchen Lin, Bing Liu | https://proceedings.mlr.press/v235/chen24l.html | ICML 2024 | Foundation Models (FMs) have demonstrated remarkable insights into the relational dynamics of the world, leading to the crucial question: how do these models acquire an understanding of world hybrid relations? Traditional statistical learning, particularly for prediction problems, may overlook the rich and inherently s... |
https://proceedings.mlr.press/v235/chen24m.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24m/chen24m.pdf | https://openreview.net/forum?id=JVhUR8q27o | Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components | https://proceedings.mlr.press/v235/chen24m.html | Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low | https://proceedings.mlr.press/v235/chen24m.html | ICML 2024 | Machine learning (ML) models in the real world typically do not exist in isolation. They are usually part of a complex system (e.g., healthcare systems, self-driving cars) containing multiple ML and black-box components. The problem of optimizing such systems, which we refer to as automated AI (AutoAI), requires us to ... |
https://proceedings.mlr.press/v235/chen24n.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24n/chen24n.pdf | https://openreview.net/forum?id=UQYXZdca92 | Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes | https://proceedings.mlr.press/v235/chen24n.html | Yifan Chen, Mark Goldstein, Mengjian Hua, Michael Samuel Albergo, Nicholas Matthew Boffi, Eric Vanden-Eijnden | https://proceedings.mlr.press/v235/chen24n.html | ICML 2024 | We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasting problem as sampling from the conditional distribution of the future system state given its current state. To this end, we leverage the fr... |
https://proceedings.mlr.press/v235/chen24o.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24o/chen24o.pdf | https://openreview.net/forum?id=iLSgF7jMtI | CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding | https://proceedings.mlr.press/v235/chen24o.html | Kaiyuan Chen, Xingzhuo Guo, Yu Zhang, Jianmin Wang, Mingsheng Long | https://proceedings.mlr.press/v235/chen24o.html | ICML 2024 | Predictive Coding (PC) is a theoretical framework in cognitive science suggesting that the human brain processes cognition through spatiotemporal prediction of visual world. Existing studies have developed spatiotemporal prediction neural networks based on the PC theroy, emulating its two core mechanisms: Correcting pr... |
https://proceedings.mlr.press/v235/chen24p.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24p/chen24p.pdf | https://openreview.net/forum?id=qIiPM5CbRY | On Interpolating Experts and Multi-Armed Bandits | https://proceedings.mlr.press/v235/chen24p.html | Houshuang Chen, Yuchen He, Chihao Zhang | https://proceedings.mlr.press/v235/chen24p.html | ICML 2024 | Learning with expert advice and multi-armed bandit are two classic online decision problems which differ on how the information is observed in each round of the game. We study a family of problems interpolating the two. For a vector $\mathbf{m}=(m_1,…,m_K)\in \mathbb N^K$, an instance of $\mathbf m$-MAB indicates that ... |
https://proceedings.mlr.press/v235/chen24q.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24q/chen24q.pdf | https://openreview.net/forum?id=IoUOhnCmlX | Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise | https://proceedings.mlr.press/v235/chen24q.html | Xi Chen, Zhewen Hou, Christopher Metzler, Arian Maleki, Shirin Jalali | https://proceedings.mlr.press/v235/chen24q.html | ICML 2024 | We investigate both the theoretical and algorithmic aspects of likelihood-based methods for recovering a complex-valued signal from multiple sets of measurements, referred to as looks, affected by speckle (multiplicative) noise. Our theoretical contributions include establishing the first existing theoretical upper bou... |
https://proceedings.mlr.press/v235/chen24r.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24r/chen24r.pdf | https://openreview.net/forum?id=LVF4P1NNwO | Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers | https://proceedings.mlr.press/v235/chen24r.html | Brian K Chen, Tianyang Hu, Hui Jin, Hwee Kuan Lee, Kenji Kawaguchi | https://proceedings.mlr.press/v235/chen24r.html | ICML 2024 | In-Context Learning (ICL) has been a powerful emergent property of large language models that has attracted increasing attention in recent years. In contrast to regular gradient-based learning, ICL is highly interpretable and does not require parameter updates. In this paper, we show that, for linearized transformer ne... |
https://proceedings.mlr.press/v235/chen24s.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24s/chen24s.pdf | https://openreview.net/forum?id=J16WEPdqhJ | Accelerated Policy Gradient for s-rectangular Robust MDPs with Large State Spaces | https://proceedings.mlr.press/v235/chen24s.html | Ziyi Chen, Heng Huang | https://proceedings.mlr.press/v235/chen24s.html | ICML 2024 | Robust Markov decision process (robust MDP) is an important machine learning framework to make a reliable policy that is robust to environmental perturbation. Despite empirical success and popularity of policy gradient methods, existing policy gradient methods require at least iteration complexity $\mathcal{O}(\epsilon... |
https://proceedings.mlr.press/v235/chen24t.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24t/chen24t.pdf | https://openreview.net/forum?id=aeXRBnLoPP | Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning | https://proceedings.mlr.press/v235/chen24t.html | Yen-Ju Chen, Nai-Chieh Huang, Ching-Pei Lee, Ping-Chun Hsieh | https://proceedings.mlr.press/v235/chen24t.html | ICML 2024 | Various acceleration approaches for Policy Gradient (PG) have been analyzed within the realm of Reinforcement Learning (RL). However, the theoretical understanding of the widely used momentum-based acceleration method on PG remains largely open. In response to this gap, we adapt the celebrated Nesterov’s accelerated gr... |
https://proceedings.mlr.press/v235/chen24u.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24u/chen24u.pdf | https://openreview.net/forum?id=d2vONO90Rw | From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning | https://proceedings.mlr.press/v235/chen24u.html | Wei Chen, Zhen Huang, Liang Xie, Binbin Lin, Houqiang Li, Le Lu, Xinmei Tian, Deng Cai, Yonggang Zhang, Wenxiao Wang, Xu Shen, Jieping Ye | https://proceedings.mlr.press/v235/chen24u.html | ICML 2024 | Large Language Models (LLMs) tend to prioritize adherence to user prompts over providing veracious responses, leading to the sycophancy issue. When challenged by users, LLMs tend to admit mistakes and provide inaccurate responses even if they initially provided the correct answer. Recent works propose to employ supervi... |
https://proceedings.mlr.press/v235/chen24v.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24v/chen24v.pdf | https://openreview.net/forum?id=x1G7ieRgRd | Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy | https://proceedings.mlr.press/v235/chen24v.html | Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu | https://proceedings.mlr.press/v235/chen24v.html | ICML 2024 | We study $L_2$ mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simultaneously handle both constraints are usually optimized for $L_\infty$ geometry and rely on random rotation or Kashin’s representation to a... |
https://proceedings.mlr.press/v235/chen24w.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24w/chen24w.pdf | https://openreview.net/forum?id=dqpg8jdA2w | Offline Transition Modeling via Contrastive Energy Learning | https://proceedings.mlr.press/v235/chen24w.html | Ruifeng Chen, Chengxing Jia, Zefang Huang, Tian-Shuo Liu, Xu-Hui Liu, Yang Yu | https://proceedings.mlr.press/v235/chen24w.html | ICML 2024 | Learning a high-quality transition model is of great importance for sequential decision-making tasks, especially in offline settings. Nevertheless, the complex behaviors of transition dynamics in real-world environments pose challenges for the standard forward models because of their inductive bias towards smooth regre... |
https://proceedings.mlr.press/v235/chen24x.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24x/chen24x.pdf | https://openreview.net/forum?id=a2uFstsHPb | Efficient Pareto Manifold Learning with Low-Rank Structure | https://proceedings.mlr.press/v235/chen24x.html | Weiyu Chen, James Kwok | https://proceedings.mlr.press/v235/chen24x.html | ICML 2024 | Multi-task learning, which optimizes performance across multiple tasks, is inherently a multi-objective optimization problem. Various algorithms are developed to provide discrete trade-off solutions on the Pareto front. Recently, continuous Pareto front approximations using a linear combination of base networks have em... |
https://proceedings.mlr.press/v235/chen24y.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24y/chen24y.pdf | https://openreview.net/forum?id=aoAPOOtN9E | Toward Adaptive Reasoning in Large Language Models with Thought Rollback | https://proceedings.mlr.press/v235/chen24y.html | Sijia Chen, Baochun Li | https://proceedings.mlr.press/v235/chen24y.html | ICML 2024 | Large language models (LLMs) have been routinely used to solve various tasks using step-by-step reasoning. However, the structure of intermediate reasoning steps, or thoughts, is rigid and unidirectional, such as chains, trees, or acyclic-directed graphs. Consequently, the resulting inflexible and forward-only reasonin... |
https://proceedings.mlr.press/v235/chen24z.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24z/chen24z.pdf | https://openreview.net/forum?id=JU3xHh1vWw | Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank | https://proceedings.mlr.press/v235/chen24z.html | Mouxiang Chen, Chenghao Liu, Zemin Liu, Zhuo Li, Jianling Sun | https://proceedings.mlr.press/v235/chen24z.html | ICML 2024 | Unbiased Learning to Rank (ULTR) aims to train unbiased ranking models from biased click logs, by explicitly modeling a generation process for user behavior and fitting click data based on examination hypothesis. Previous research found empirically that the true latent relevance is mostly recoverable through click fitt... |
https://proceedings.mlr.press/v235/chen24aa.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24aa/chen24aa.pdf | https://openreview.net/forum?id=bBzlapzeR1 | High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization | https://proceedings.mlr.press/v235/chen24aa.html | Yihang Chen, Fanghui Liu, Taiji Suzuki, Volkan Cevher | https://proceedings.mlr.press/v235/chen24aa.html | ICML 2024 | This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposition, we theoretically demonstrate that the re-weighting strateg... |
https://proceedings.mlr.press/v235/chen24ab.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ab/chen24ab.pdf | https://openreview.net/forum?id=0uUHfhXdnH | DiJiang: Efficient Large Language Models through Compact Kernelization | https://proceedings.mlr.press/v235/chen24ab.html | Hanting Chen, Liu Zhicheng, Xutao Wang, Yuchuan Tian, Yunhe Wang | https://proceedings.mlr.press/v235/chen24ab.html | ICML 2024 | In an effort to reduce the computational load of Transformers, research on linear attention has gained significant momentum. However, the improvement strategies for attention mechanisms typically necessitate extensive retraining, which is impractical for large language models with a vast array of parameters. In this pa... |
https://proceedings.mlr.press/v235/chen24ac.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ac/chen24ac.pdf | https://openreview.net/forum?id=Y5Zi59N265 | GeoMFormer: A General Architecture for Geometric Molecular Representation Learning | https://proceedings.mlr.press/v235/chen24ac.html | Tianlang Chen, Shengjie Luo, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang | https://proceedings.mlr.press/v235/chen24ac.html | ICML 2024 | Molecular modeling, a central topic in quantum mechanics, aims to accurately calculate the properties and simulate the behaviors of molecular systems. The molecular model is governed by physical laws, which impose geometric constraints such as invariance and equivariance to coordinate rotation and translation. While nu... |
https://proceedings.mlr.press/v235/chen24ad.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ad/chen24ad.pdf | https://openreview.net/forum?id=v1I4zRAjMb | TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision | https://proceedings.mlr.press/v235/chen24ad.html | Zhuo Chen, Jacob Mccarran, Esteban Vizcaino, Marin Soljacic, Di Luo | https://proceedings.mlr.press/v235/chen24ad.html | ICML 2024 | Partial differential equations (PDEs) are instrumental for modeling dynamical systems in science and engineering. The advent of neural networks has initiated a significant shift in tackling these complexities though challenges in accuracy persist, especially for initial value problems. In this paper, we introduce the T... |
https://proceedings.mlr.press/v235/chen24ae.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ae/chen24ae.pdf | https://openreview.net/forum?id=xFk0w9zoV3 | EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism | https://proceedings.mlr.press/v235/chen24ae.html | Yanxi Chen, Xuchen Pan, Yaliang Li, Bolin Ding, Jingren Zhou | https://proceedings.mlr.press/v235/chen24ae.html | ICML 2024 | We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs). While recent works have shown preliminary evidence for the efficacy of early exiting in accelerating LLM inference, EE-LLM makes a foundational step towards scaling up early-exit LLMs by supporting their tr... |
https://proceedings.mlr.press/v235/chen24af.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24af/chen24af.pdf | https://openreview.net/forum?id=AxmefV2NEf | TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning | https://proceedings.mlr.press/v235/chen24af.html | Xiwen Chen, Peijie Qiu, Wenhui Zhu, Huayu Li, Hao Wang, Aristeidis Sotiras, Yalin Wang, Abolfazl Razi | https://proceedings.mlr.press/v235/chen24af.html | ICML 2024 | Deep neural networks, including transformers and convolutional neural networks (CNNs), have significantly improved multivariate time series classification (MTSC). However, these methods often rely on supervised learning, which does not fully account for the sparsity and locality of patterns in time series data (e.g., q... |
https://proceedings.mlr.press/v235/chen24ag.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ag/chen24ag.pdf | https://openreview.net/forum?id=PHUAG63Efe | AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning | https://proceedings.mlr.press/v235/chen24ag.html | Dong Chen, Hongyuan Qu, Guangwu Xu | https://proceedings.mlr.press/v235/chen24ag.html | ICML 2024 | Privacy attacks and poisoning attacks are two of the thorniest problems in federation learning (FL). Homomorphic encryption (HE), which allows certain mathematical operations to be done in the ciphertext state, provides a way to solve these two problems simultaneously. However, existing Paillier-based and CKKS-based pr... |
https://proceedings.mlr.press/v235/chen24ah.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ah/chen24ah.pdf | https://openreview.net/forum?id=ffLblkoCw8 | MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models | https://proceedings.mlr.press/v235/chen24ah.html | Justin Chen, Swarnadeep Saha, Elias Stengel-Eskin, Mohit Bansal | https://proceedings.mlr.press/v235/chen24ah.html | ICML 2024 | Multi-agent interactions between Large Language Model (LLM) agents have shown major improvements on diverse reasoning tasks. However, these involve long generations from multiple models across several rounds, making them expensive. Moreover, these multi-agent approaches fail to provide a final, single model for efficie... |
https://proceedings.mlr.press/v235/chen24ai.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ai/chen24ai.pdf | https://openreview.net/forum?id=sLZzFTMWSt | CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process | https://proceedings.mlr.press/v235/chen24ai.html | Guangyi Chen, Yifan Shen, Zhenhao Chen, Xiangchen Song, Yuewen Sun, Weiran Yao, Xiao Liu, Kun Zhang | https://proceedings.mlr.press/v235/chen24ai.html | ICML 2024 | Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning. While some recent methods can robustly identify these latent causal variables, they rely on strict assumptions about the invertible generation process from latent v... |
https://proceedings.mlr.press/v235/chen24aj.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24aj/chen24aj.pdf | https://openreview.net/forum?id=d2f2sCXQuI | GRATH: Gradual Self-Truthifying for Large Language Models | https://proceedings.mlr.press/v235/chen24aj.html | Weixin Chen, Dawn Song, Bo Li | https://proceedings.mlr.press/v235/chen24aj.html | ICML 2024 | Truthfulness is paramount for large language models (LLMs) as they are increasingly deployed in real-world applications. However, existing LLMs still struggle with generating truthful content, as evidenced by their modest performance on benchmarks like TruthfulQA. To address this issue, we propose GRAdual self-truTHify... |
https://proceedings.mlr.press/v235/chen24ak.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ak/chen24ak.pdf | https://openreview.net/forum?id=QhHMx51ir6 | Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts | https://proceedings.mlr.press/v235/chen24ak.html | Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying Wei | https://proceedings.mlr.press/v235/chen24ak.html | ICML 2024 | Recent successes suggest that parameter-efficient fine-tuning of foundation models is becoming the state-of-the-art method for transfer learning in vision, gradually replacing the rich literature of alternatives such as meta-learning. In trying to harness the best of both worlds, meta-tuning introduces a subsequent opt... |
https://proceedings.mlr.press/v235/chen24al.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24al/chen24al.pdf | https://openreview.net/forum?id=yHs3jIPgaF | Performative Prediction with Bandit Feedback: Learning through Reparameterization | https://proceedings.mlr.press/v235/chen24al.html | Yatong Chen, Wei Tang, Chien-Ju Ho, Yang Liu | https://proceedings.mlr.press/v235/chen24al.html | ICML 2024 | Performative prediction, as introduced by Perdomo et al., is a framework for studying social prediction in which the data distribution itself changes in response to the deployment of a model. Existing work in this field usually hinges on three assumptions that are easily violated in practice: that the performative risk... |
https://proceedings.mlr.press/v235/chen24am.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24am/chen24am.pdf | https://openreview.net/forum?id=4RqG4K5UwL | Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes | https://proceedings.mlr.press/v235/chen24am.html | Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan Suykens | https://proceedings.mlr.press/v235/chen24am.html | ICML 2024 | While the great capability of Transformers significantly boosts prediction accuracy, it could also yield overconfident predictions and require calibrated uncertainty estimation, which can be commonly tackled by Gaussian processes (GPs). Existing works apply GPs with symmetric kernels under variational inference to the ... |
https://proceedings.mlr.press/v235/chen24an.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24an/chen24an.pdf | https://openreview.net/forum?id=E41gvBG4s6 | Recovering Labels from Local Updates in Federated Learning | https://proceedings.mlr.press/v235/chen24an.html | Huancheng Chen, Haris Vikalo | https://proceedings.mlr.press/v235/chen24an.html | ICML 2024 | Gradient inversion (GI) attacks present a threat to the privacy of clients in federated learning (FL) by aiming to enable reconstruction of the clients’ data from communicated model updates. A number of such techniques attempts to accelerate data recovery by first reconstructing labels of the samples used in local trai... |
https://proceedings.mlr.press/v235/chen24ao.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ao/chen24ao.pdf | https://openreview.net/forum?id=gjgRKbdYR7 | SelfIE: Self-Interpretation of Large Language Model Embeddings | https://proceedings.mlr.press/v235/chen24ao.html | Haozhe Chen, Carl Vondrick, Chengzhi Mao | https://proceedings.mlr.press/v235/chen24ao.html | ICML 2024 | How do large language models (LLMs) obtain their answers? The ability to explain and control an LLM’s reasoning process is key for reliability, transparency, and future model developments. We propose SelfIE (Self-Interpretation of Embeddings), a framework that enables LLMs to interpret their own embeddings in natural l... |
https://proceedings.mlr.press/v235/chen24ap.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ap/chen24ap.pdf | https://openreview.net/forum?id=RuH78kOcDi | Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy | https://proceedings.mlr.press/v235/chen24ap.html | Ziqin Chen, Yongqiang Wang | https://proceedings.mlr.press/v235/chen24ap.html | ICML 2024 | Decentralized bilevel optimization based machine learning techniques are achieving remarkable success in a wide variety of domains. However, the intensive exchange of information (involving nested-loops of consensus or communication iterations) in existing decentralized bilevel optimization algorithms leads to a great ... |
https://proceedings.mlr.press/v235/chen24aq.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24aq/chen24aq.pdf | https://openreview.net/forum?id=hQpUhySEJi | Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments | https://proceedings.mlr.press/v235/chen24aq.html | Runfa Chen, Ling Wang, Yu Du, Tianrui Xue, Fuchun Sun, Jianwei Zhang, Wenbing Huang | https://proceedings.mlr.press/v235/chen24aq.html | ICML 2024 | Learning policies for multi-entity systems in 3D environments is far more complicated against single-entity scenarios, due to the exponential expansion of the global state space as the number of entities increases. One potential solution of alleviating the exponential complexity is dividing the global space into indepe... |
https://proceedings.mlr.press/v235/chen24ar.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ar/chen24ar.pdf | https://openreview.net/forum?id=7sgqXa4aNM | A General Framework for Learning from Weak Supervision | https://proceedings.mlr.press/v235/chen24ar.html | Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj | https://proceedings.mlr.press/v235/chen24ar.html | ICML 2024 | Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical deployment. This paper introduces a general framework for learning from weak supervision (GLWS) with ... |
https://proceedings.mlr.press/v235/chen24as.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24as/chen24as.pdf | https://openreview.net/forum?id=OnidGtOhg3 | Diffusion Model-Augmented Behavioral Cloning | https://proceedings.mlr.press/v235/chen24as.html | Shang-Fu Chen, Hsiang-Chun Wang, Ming-Hao Hsu, Chun-Mao Lai, Shao-Hua Sun | https://proceedings.mlr.press/v235/chen24as.html | ICML 2024 | Imitation learning addresses the challenge of learning by observing an expert’s demonstrations without access to reward signals from environments. Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e.g., ... |
https://proceedings.mlr.press/v235/chen24at.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24at/chen24at.pdf | https://openreview.net/forum?id=Rp8R9C0Sth | AutoOS: Make Your OS More Powerful by Exploiting Large Language Models | https://proceedings.mlr.press/v235/chen24at.html | Huilai Chen, Yuanbo Wen, Limin Cheng, Shouxu Kuang, Yumeng Liu, Weijia Li, Ling Li, Rui Zhang, Xinkai Song, Wei Li, Qi Guo, Yunji Chen | https://proceedings.mlr.press/v235/chen24at.html | ICML 2024 | With the rapid development of Artificial Intelligence of Things (AIoT), customizing and optimizing operating system (OS) kernel configurations for various AIoT application scenarios is crucial for maximizing system performance. However, existing approaches falter due to the overwhelming problem complexity (i.e., over 1... |
https://proceedings.mlr.press/v235/chen24au.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24au/chen24au.pdf | https://openreview.net/forum?id=VOcsmIBiXE | Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning | https://proceedings.mlr.press/v235/chen24au.html | Junfeng Chen, Kailiang Wu | https://proceedings.mlr.press/v235/chen24au.html | ICML 2024 | Operator learning for Partial Differential Equations (PDEs) is rapidly emerging as a promising approach for surrogate modeling of intricate systems. Transformers with the self-attention mechanism—a powerful tool originally designed for natural language processing—have recently been adapted for operator learning. Howeve... |
https://proceedings.mlr.press/v235/chen24av.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24av/chen24av.pdf | https://openreview.net/forum?id=s3e8poX3kb | In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation | https://proceedings.mlr.press/v235/chen24av.html | Shiqi Chen, Miao Xiong, Junteng Liu, Zhengxuan Wu, Teng Xiao, Siyang Gao, Junxian He | https://proceedings.mlr.press/v235/chen24av.html | ICML 2024 | Large language models (LLMs) frequently hallucinate, e.g., making factual errors, yet our understanding of why they make these errors remains limited. In this study, we aim to understand the underlying mechanisms of LLM hallucinations from the perspective of inner representations. We discover a pattern associated with ... |
https://proceedings.mlr.press/v235/chen24aw.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24aw/chen24aw.pdf | https://openreview.net/forum?id=pQyoBWA146 | Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting | https://proceedings.mlr.press/v235/chen24aw.html | Anthony Chen, Huanrui Yang, Yulu Gan, Denis A Gudovskiy, Zhen Dong, Haofan Wang, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, Shanghang Zhang | https://proceedings.mlr.press/v235/chen24aw.html | ICML 2024 | Uncertainty estimation is crucial for deep learning models to detect out-of-distribution (OOD) inputs. However, the naive deep learning classifiers produce uncalibrated uncertainty for OOD data. Improving the uncertainty estimation typically requires external data for OOD-aware training or considerable costs to build a... |
https://proceedings.mlr.press/v235/chen24ax.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ax/chen24ax.pdf | https://openreview.net/forum?id=DJdVzxemdA | Deep Demonstration Tracing: Learning Generalizable Imitator Policy for Runtime Imitation from a Single Demonstration | https://proceedings.mlr.press/v235/chen24ax.html | Xiong-Hui Chen, Junyin Ye, Hang Zhao, Yi-Chen Li, Xu-Hui Liu, Haoran Shi, Yu-Yan Xu, Zhihao Ye, Si-Hang Yang, Yang Yu, Anqi Huang, Kai Xu, Zongzhang Zhang | https://proceedings.mlr.press/v235/chen24ax.html | ICML 2024 | One-shot imitation learning (OSIL) is to learn an imitator agent that can execute multiple tasks with only a single demonstration. In real-world scenario, the environment is dynamic, e.g., unexpected changes can occur after demonstration. Thus, achieving generalization of the imitator agent is crucial as agents would i... |
https://proceedings.mlr.press/v235/chen24ay.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ay/chen24ay.pdf | https://openreview.net/forum?id=oRLwyayrh1 | DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images | https://proceedings.mlr.press/v235/chen24ay.html | Baoying Chen, Jishen Zeng, Jianquan Yang, Rui Yang | https://proceedings.mlr.press/v235/chen24ay.html | ICML 2024 | Diffusion models have made significant strides in visual content generation but also raised increasing demands on generated image detection. Existing detection methods have achieved considerable progress, but they usually suffer a significant decline in accuracy when detecting images generated by an unseen diffusion mo... |
https://proceedings.mlr.press/v235/chen24az.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24az/chen24az.pdf | https://openreview.net/forum?id=yShA4VPYZB | $\rm E(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning | https://proceedings.mlr.press/v235/chen24az.html | Dingyang Chen, Qi Zhang | https://proceedings.mlr.press/v235/chen24az.html | ICML 2024 | Identification and analysis of symmetrical patterns in the natural world have led to significant discoveries across various scientific fields, such as the formulation of gravitational laws in physics and advancements in the study of chemical structures. In this paper, we focus on exploiting Euclidean symmetries inheren... |
https://proceedings.mlr.press/v235/chen24ba.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24ba/chen24ba.pdf | https://openreview.net/forum?id=jrHUbftLd6 | FedMBridge: Bridgeable Multimodal Federated Learning | https://proceedings.mlr.press/v235/chen24ba.html | Jiayi Chen, Aidong Zhang | https://proceedings.mlr.press/v235/chen24ba.html | ICML 2024 | Multimodal Federated Learning (MFL) addresses the setup of multiple clients with diversified modality types (e.g. image, text, video, and audio) working together to improve their local personal models in a data-privacy manner. Prior MFL works rely on restrictive compositional neural architecture designs to ensure inter... |
https://proceedings.mlr.press/v235/chen24bb.html | https://raw.githubusercontent.com/mlresearch/v235/main/assets/chen24bb/chen24bb.pdf | https://openreview.net/forum?id=rkYOxLLv2x | Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness | https://proceedings.mlr.press/v235/chen24bb.html | Honghao Chen, Yurong Zhang, Xiaokun Feng, Xiangxiang Chu, Kaiqi Huang | https://proceedings.mlr.press/v235/chen24bb.html | ICML 2024 | Robustness is a vital aspect to consider when deploying deep learning models into the wild. Numerous studies have been dedicated to the study of the robustness of vision transformers (ViTs), which have dominated as the mainstream backbone choice for vision tasks since the dawn of 2020s. Recently, some large kernel conv... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.