184 citations · 931 across the 54 of their papers we have counts for
55 papers
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback
Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel +4
We present a novel unified bilevel optimization-based framework, \textsf{PARL}, formulated to address the recently highlighted critical issue of policy alignment in reinforcement l…
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Ruijie Zheng, Xiyao Wang, Yanchao Sun +5
Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle. Prior works have attempted to address t…
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
Paiheng Xu, Yuhang Zhou, Bang An +2
Given the growing concerns about fairness in machine learning and the impressive performance of Graph Neural Networks (GNNs) on graph data learning, algorithmic fairness in GNNs ha…
On the Possibilities of AI-Generated Text Detection
Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu +3
Our work addresses the critical issue of distinguishing text generated by Large Language Models (LLMs) from human-produced text, a task essential for numerous applications. Despite…
Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
Ruijie Zheng, Xiyao Wang, Huazhe Xu +1
Probabilistic dynamics model ensemble is widely used in existing model-based reinforcement learning methods as it outperforms a single dynamics model in both asymptotic performance…
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness
Yuancheng Xu, Yanchao Sun, Micah Goldblum +2
The robustness of a deep classifier can be characterized by its margins: the decision boundary's distances to natural data points. However, it is unclear whether existing robust tr…
SMART: Self-supervised Multi-task pretrAining with contRol Transformers
Yanchao Sun, Shuang Ma, Ratnesh Madaan +3
Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining repr…
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning
Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel +3
Directed Exploration is a crucial challenge in reinforcement learning (RL), especially when rewards are sparse. Information-directed sampling (IDS), which optimizes the information…
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
Marco Bornstein, Tahseen Rabbani, Evan Wang +2
The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train…
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
Yongyuan Liang, Yanchao Sun, Ruijie Zheng +1
Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be particularly vulnerable to adversarial perturbations on input observations. Therefore, it i…
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Arpit Bansal, Eitan Borgnia, Hong-Min Chu +6
Standard diffusion models involve an image transform -- adding Gaussian noise -- and an image restoration operator that inverts this degradation. We observe that the generative beh…
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh +4
Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained commu…
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Xiyao Wang, Wichayaporn Wongkamjan, Furong Huang
Model-based reinforcement learning (RL) often achieves higher sample efficiency in practice than model-free RL by learning a dynamics model to generate samples for policy learning.…
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
Bang An, Zora Che, Mucong Ding +1
The increasing reliance on ML models in high-stakes tasks has raised a major concern on fairness violations. Although there has been a surge of work that improves algorithmic fairn…
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus
Amrit Singh Bedi, Chen Fan, Alec Koppel +4
In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as…
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel +4
Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the set…
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
Arpit Bansal, Avi Schwarzschild, Eitan Borgnia +4
Machine learning systems perform well on pattern matching tasks, but their ability to perform algorithmic or logical reasoning is not well understood. One important reasoning capab…
Transfer RL across Observation Feature Spaces via Model-Based Regularization
Yanchao Sun, Ruijie Zheng, Xiyao Wang +2
In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramati…
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
Sicheng Zhu, Bang An, Furong Huang
Machine learning models that are developed with invariance to certain types of data transformations have demonstrated superior generalization performance in practice. However, the…
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Mucong Ding, Kezhi Kong, Jingling Li +4
Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To sca…
Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching
Tahseen Rabbani, Brandon Feng, Marco Bornstein +5
Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge. In centralized FL, the parameters of a global architecture (such as a deep n…
Practical and Fast Momentum-Based Power Methods
Tahseen Rabbani, Apollo Jain, Arjun Rajkumar +1
The power method is a classical algorithm with broad applications in machine learning tasks, including streaming PCA, spectral clustering, and low-rank matrix approximation. The di…
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
Roman Levin, Manli Shu, Eitan Borgnia +3
Conventional saliency maps highlight input features to which neural network predictions are highly sensitive. We take a different approach to saliency, in which we identify and ana…
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders
Huimin Zeng, Jiahao Su, Furong Huang
Randomized Smoothing (RS), being one of few provable defenses, has been showing great effectiveness and scalability in terms of defending against -norm adversarial perturba…