151 citations · 542 across the 33 of their papers we have counts for
34 papers
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Zhizhou Ren, Anji Liu, Yitao Liang +2
Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning t…
Is Self-Supervised Learning More Robust Than Supervised Learning?
Yuanyi Zhong, Haoran Tang, Junkun Chen +2
Self-supervised contrastive learning is a powerful tool to learn visual representation without labels. Prior work has primarily focused on evaluating the recognition accuracy of va…
Imitation Learning from Observations under Transition Model Disparity
Tanmay Gangwani, Yuan Zhou, Jian Peng
Learning to perform tasks by leveraging a dataset of expert observations, also known as imitation learning from observations (ILO), is an important paradigm for learning skills wit…
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing
Shitong Luo, Jiahan Li, Jiaqi Guan +4
Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, simplicity, and e…
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Zhizhou Ren, Ruihan Guo, Yuan Zhou +1
Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to fut…
Hindsight Foresight Relabeling for Meta-Reinforcement Learning
Michael Wan, Jian Peng, Tanmay Gangwani
Meta-reinforcement learning (meta-RL) algorithms allow for agents to learn new behaviors from small amounts of experience, mitigating the sample inefficiency problem in RL. However…
Coordinate-wise Control Variates for Deep Policy Gradients
Yuanyi Zhong, Yuan Zhou, Jian Peng
The control variates (CV) method is widely used in policy gradient estimation to reduce the variance of the gradient estimators in practice. A control variate is applied by subtrac…
Off-Policy Reinforcement Learning with Delayed Rewards
Beining Han, Zhizhou Ren, Zuofan Wu +2
We study deep reinforcement learning (RL) algorithms with delayed rewards. In many real-world tasks, instant rewards are often not readily accessible or even defined immediately af…
Learning Neural Generative Dynamics for Molecular Conformation Generation
Minkai Xu, Shitong Luo, Yoshua Bengio +2
We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationa…
DAP: Detection-Aware Pre-training with Weak Supervision
Yuanyi Zhong, Jianfeng Wang, Lijuan Wang +3
This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is speci…
Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer
Yuanyi Zhong, Jianfeng Wang, Jian Peng +1
In this paper, we propose an effective knowledge transfer framework to boost the weakly supervised object detection accuracy with the help of an external fully-annotated source dat…
Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion
Yi Chen, Jinglin Chen, Jing Dong +2
Langevin diffusion is a powerful method for nonconvex optimization, which enables the escape from local minima by injecting noise into the gradient. In particular, the temperature…
Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch
Michael Wan, Tanmay Gangwani, Jian Peng
Deep reinforcement learning (RL) algorithms have achieved great success on a wide variety of sequential decision-making tasks. However, many of these algorithms suffer from high sa…
Stein Variational Inference for Discrete Distributions
Jun Han, Fan Ding, Xianglong Liu +3
Gradient-based approximate inference methods, such as Stein variational gradient descent (SVGD), provide simple and general-purpose inference engines for differentiable continuous…
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation
Jian Peng, Bo Tang, Hao Jiang +4
Artificial neural networks face the well-known problem of catastrophic forgetting. What's worse, the degradation of previously learned skills becomes more severe as the task sequen…
-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
Kefan Dong, Jian Peng, Yining Wang +1
In this paper, we consider the problem of online learning of Markov decision processes (MDPs) with very large state spaces. Under the assumptions of realizable function approximati…
A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization
Yucheng Chen, Matus Telgarsky, Chao Zhang +3
This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs). The app…
Exploration via Hindsight Goal Generation
Zhizhou Ren, Kefan Dong, Yuan Zhou +2
Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a func…
Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning
Yang Liu, Yunan Luo, Yuanyi Zhong +3
Recent advances in deep reinforcement learning algorithms have shown great potential and success for solving many challenging real-world problems, including Go game and robotic app…
Stochastic Variance Reduction for Deep Q-learning
Wei-Ye Zhao, Xi-Ya Guan, Yang Liu +2
Recent advances in deep reinforcement learning have achieved human-level performance on a variety of real-world applications. However, the current algorithms still suffer from poor…
Knowledge Flow: Improve Upon Your Teachers
Iou-Jen Liu, Jian Peng, Alexander G. Schwing
A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an i…
Overcoming Catastrophic Forgetting by Soft Parameter Pruning
Jian Peng, Jiang Hao, Zhuo Li +5
Catastrophic forgetting is a challenge issue in continual learning when a deep neural network forgets the knowledge acquired from the former task after learning on subsequent tasks…
emrQA: A Large Corpus for Question Answering on Electronic Medical Records
Anusri Pampari, Preethi Raghavan, Jennifer Liang +1
We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an…
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
Yuan Xie, Boyi Liu, Qiang Liu +3
When learning from a batch of logged bandit feedback, the discrepancy between the policy to be learned and the off-policy training data imposes statistical and computational challe…