no code implementations • 7 May 2023 • Kegang Wang, Yantao Wei, Mingwen Tong, Jie Gao, Yi Tian, YuJian Ma, ZhongJin Zhao
In recent years, due to the widespread use of internet videos, physiological remote sensing has gained more and more attention in the fields of affective computing and telemedicine.
no code implementations • 30 Dec 2022 • Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra
We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system.
no code implementations • 3 Apr 2022 • Ali Jadbabaie, Haochuan Li, Jian Qian, Yi Tian
In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model.
no code implementations • NeurIPS 2021 • Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie
We provide a first-order oracle complexity lower bound for finding stationary points of min-max optimization problems where the objective function is smooth, nonconvex in the minimization variable, and strongly concave in the maximization variable.
no code implementations • 5 Feb 2021 • Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra
To our knowledge, this work provides the first provably efficient algorithms for vector-valued Markov games and our theoretical guarantees are near-optimal.
no code implementations • 28 Oct 2020 • Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra
We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable.
no code implementations • NeurIPS 2020 • Yi Tian, Jian Qian, Suvrit Sra
We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components.
no code implementations • CVPR 2018 • Yansong Tang, Yi Tian, Jiwen Lu, Peiyang Li, Jie zhou
In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in sequences for recognizing actions.
Ranked #3 on Skeleton Based Action Recognition on UT-Kinect