70 citations · 880 across the 62 of their papers we have counts for
62 papers
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
Peijie Sun, Le Wu, Kun Zhang +2
While effective in recommendation tasks, collaborative filtering (CF) techniques face the challenge of data sparsity. Researchers have begun leveraging contrastive learning to intr…
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
Zhenghao Xu, Xiang Ji, Minshuo Chen +2
Policy gradient methods equipped with deep neural networks have achieved great success in solving high-dimensional reinforcement learning (RL) problems. However, current analyses c…
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Yikuan Li, Chengsheng Mao, Kaixuan Huang +4
Scarcity of health care resources could result in the unavoidable consequence of rationing. For example, ventilators are often limited in supply, especially during public health em…
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks
Siyu Chen, Mengdi Wang, Zhuoran Yang
We study reinforcement learning (RL) for learning a Quantal Stackelberg Equilibrium (QSE) in an episodic Markov game with a leader-follower structure. In specific, at the outset of…
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
Zixuan Zhang, Minshuo Chen, Mengdi Wang +2
Existing theories on deep nonparametric regression have shown that when the input data lie on a low-dimensional manifold, deep neural networks can adapt to the intrinsic data struc…
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
Jiacheng Guo, Zihao Li, Huazheng Wang +3
In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dim…