b'Yifei Min'

research

∙ 05/10/2023

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

We study multi-agent reinforcement learning in the setting of episodic M...

3 Yifei Min, et al. ∙

research

∙ 04/06/2023

Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts

Integrating high-level semantically correlated contents and low-level an...

0 Chenyu You, et al. ∙

research

∙ 04/05/2023

ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast

Medical data often exhibits long-tail distributions with heavy class imb...

0 Chenyu You, et al. ∙

research

∙ 02/03/2023

Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

For medical image segmentation, contrastive learning is the dominant pra...

0 Chenyu You, et al. ∙

research

∙ 07/07/2022

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits

We study federated contextual linear bandits, where M agents cooperate w...

6 Jiafan He, et al. ∙

research

∙ 05/26/2022

Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes

We study offline reinforcement learning (RL) in partially observable Mar...

6 Miao Lu, et al. ∙

research

∙ 03/07/2022

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets

We study a Markov matching market involving a planner and a set of strat...

2 Yifei Min, et al. ∙

research

∙ 10/25/2021

Learning Stochastic Shortest Path with Linear Function Approximation

We study the stochastic shortest path (SSP) problem in reinforcement lea...

11 Yifei Min, et al. ∙

research

∙ 06/22/2021

Variance-Aware Off-Policy Evaluation with Linear Function Approximation

We study the off-policy evaluation (OPE) problem in reinforcement learni...

7 Yifei Min, et al. ∙

research

∙ 08/03/2020

Multiple Descent: Design Your Own Generalization Curve

This paper explores the generalization loss of linear regression in vari...

2 Lin Chen, et al. ∙

research

∙ 02/25/2020

The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization

Despite remarkable success, deep neural networks are sensitive to human-...

10 Yifei Min, et al. ∙

research

∙ 02/11/2020

More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models

Despite remarkable success in practice, modern machine learning models h...

38 Lin Chen, et al. ∙

Yifei Min

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