6 citations · 6 across the 6 of their papers we have counts for
6 papers
Learnable Mixed Nash Equilibria are Collectively Rational
Geelon So, Yi-An Ma
We extend the study of learning in games to dynamics that exhibit non-asymptotic stability. We do so through the notion of uniform stability, which is concerned with equilibria of…
Learning to Steer Learners in Games
Yizhou Zhang, Yi-An Ma, Eric Mazumdar
We consider the problem of learning to exploit learning algorithms through repeated interactions in games. Specifically, we focus on the case of repeated two player, finite-action…
Classical Verification of Quantum Learning Advantages with Noises
Yinghao Ma, Jiaxi Su, Dong-Ling Deng
Classical verification of quantum learning allows classical clients to reliably leverage quantum computing advantages by interacting with untrusted quantum servers. Yet, current qu…
A Gradient-Based Optimization Method Using the Koopman Operator
Mengqi Hu, Bian Li, Yi-An Ma +2
In this paper, we propose a novel approach to solving optimization problems by reformulating the optimization problem into a dynamical system, followed by the adaptive spectral Koo…
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization
Chaobing Song, Yong Jiang, Yi Ma
In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named \emph{Variance Reduction via Accelerated Dual Averaging (VRADA)}. In both gene…
Breaking the Optimal Rate for a Class of Minimax Problems
Chaobing Song, Yong Jiang, Yi Ma
It is known that for convex optimization , the best possible rate of first order accelerated methods is . However, for th…