610 citations · 2k across the 74 of their papers we have counts for
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Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference
Dongyan Huo, Yudong Chen, Qiaomin Xie
In this paper, we study the effectiveness of using a constant stepsize in statistical inference via linear stochastic approximation (LSA) algorithms with Markovian data. After esta…
Span-Based Optimal Sample Complexity for Average Reward MDPs
Matthew Zurek, Yudong Chen
We study the sample complexity of learning an -optimal policy in an average-reward Markov decision process (MDP) under a generative model. We establish the complexity…
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
Young Wu, Jeremy McMahan, Yiding Chen +3
We study the game modification problem, where a benevolent game designer or a malevolent adversary modifies the reward function of a zero-sum Markov game so that a target determini…
Gap-Free Clustering: Sensitivity and Robustness of SDP
Matthew Zurek, Yudong Chen
We study graph clustering in the Stochastic Block Model (SBM) in the presence of both large clusters and small, unrecoverable clusters. Previous convex relaxation approaches achiev…
VISER: A Tractable Solution Concept for Games with Information Asymmetry
Jeremy McMahan, Young Wu, Yudong Chen +2
Many real-world games suffer from information asymmetry: one player is only aware of their own payoffs while the other player has the full game information. Examples include the cr…
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Angeliki Giannou, Yudong Chen +1
For min-max optimization and variational inequalities problems (VIP) encountered in diverse machine learning tasks, Stochastic Extragradient (SEG) and Stochastic Gradient Descent A…