Search Results for author: Yifei Min

Found 14 papers, 3 papers with code

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

no code implementations10 May 2023 Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu

We study multi-agent reinforcement learning in the setting of episodic Markov decision processes, where multiple agents cooperate via communication through a central server.

Multi-agent Reinforcement Learning reinforcement-learning

Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts

1 code implementation6 Apr 2023 Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S. Duncan

Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation.

Image Segmentation Medical Image Segmentation +3

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

2 code implementations5 Apr 2023 Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, Jasjeet S. Sekhon, James S. Duncan

In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation.

Contrastive Learning Image Segmentation +2

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

no code implementations27 Sep 2022 Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Haoran Su, Xiaoran Zhang, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan

Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.

Anatomy Contrastive Learning +4

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

no code implementations7 Jul 2022 Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu

To the best of our knowledge, this is the first provably efficient algorithm that allows fully asynchronous communication for federated contextual linear bandits, while achieving the same regret guarantee as in the single-agent setting.

Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation

1 code implementation6 Jun 2022 Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S. Duncan

In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation.

Contrastive Learning Image Segmentation +3

Learning Stochastic Shortest Path with Linear Function Approximation

no code implementations25 Oct 2021 Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu

To the best of our knowledge, this is the first algorithm with a sublinear regret guarantee for learning linear mixture SSP.

Variance-Aware Off-Policy Evaluation with Linear Function Approximation

no code implementations NeurIPS 2021 Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu

We study the off-policy evaluation (OPE) problem in reinforcement learning with linear function approximation, which aims to estimate the value function of a target policy based on the offline data collected by a behavior policy.

Off-policy evaluation

Multiple Descent: Design Your Own Generalization Curve

no code implementations NeurIPS 2021 Lin Chen, Yifei Min, Mikhail Belkin, Amin Karbasi

This paper explores the generalization loss of linear regression in variably parameterized families of models, both under-parameterized and over-parameterized.

regression

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

no code implementations25 Feb 2020 Yifei Min, Lin Chen, Amin Karbasi

In the medium adversary regime, with more training data, the generalization loss exhibits a double descent curve, which implies the existence of an intermediate stage where more training data hurts the generalization.

Classification General Classification +1

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

no code implementations ICML 2020 Lin Chen, Yifei Min, Mingrui Zhang, Amin Karbasi

Despite remarkable success in practice, modern machine learning models have been found to be susceptible to adversarial attacks that make human-imperceptible perturbations to the data, but result in serious and potentially dangerous prediction errors.

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