1 code implementation • 12 Mar 2024 • Chenyu You, Yifei Min, Weicheng Dai, Jasjeet S. Sekhon, Lawrence Staib, James S. Duncan
As a piloting study, this work focuses on exploring mitigating the reliance on spurious features for CLIP without using any group annotation.
no code implementations • 10 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.
1 code implementation • 6 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.
2 code implementations • 5 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.
no code implementations • 24 Feb 2023 • Ruitu Xu, Yifei Min, Tianhao Wang, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market.
no code implementations • 27 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.
no code implementations • 7 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.
1 code implementation • 6 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.
no code implementations • 26 May 2022 • Miao Lu, Yifei Min, Zhaoran Wang, Zhuoran Yang
We study offline reinforcement learning (RL) in partially observable Markov decision processes.
no code implementations • 7 Mar 2022 • Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market.
no code implementations • 25 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.
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.
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.
no code implementations • 25 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.
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.