10 citations · 22 across the 8 of their papers we have counts for
8 papers
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
Yi Tian, Kaiqing Zhang, Russ Tedrake +1
We study the problem of state representation learning for control from partial and potentially high-dimensional observations. We approach this problem via cost-driven state represe…
Convex and Non-convex Optimization Under Generalized Smoothness
Haochuan Li, Jian Qian, Yi Tian +2
Classical analysis of convex and non-convex optimization methods often requires the Lipshitzness of the gradient, which limits the analysis to functions bounded by quadratics. Rece…
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part I
Yi Tian, Kaiqing Zhang, Russ Tedrake +1
We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a…
Byzantine-Robust Federated Linear Bandits
Ali Jadbabaie, Haochuan Li, Jian Qian +1
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 mode…
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization
Haochuan Li, Yi Tian, Jingzhao Zhang +1
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 minim…
Provably Efficient Algorithms for Multi-Objective Competitive RL
Tiancheng Yu, Yi Tian, Jingzhao Zhang +1
We study multi-objective reinforcement learning (RL) where an agent's reward is represented as a vector. In settings where an agent competes against opponents, its performance is m…
Online Learning in Unknown Markov Games
Yi Tian, Yuanhao Wang, Tiancheng Yu +1
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. We show…
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
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. Assumin…