69 citations · 393 across the 21 of their papers we have counts for
7 papers · 1 filter
Generalization Bounds for Stochastic Saddle Point Problems
Junyu Zhang, Mingyi Hong, Mengdi Wang +1
This paper studies the generalization bounds for the empirical saddle point (ESP) solution to stochastic saddle point (SSP) problems. For SSP with Lipschitz continuous and strongly…
A Single Time-Scale Stochastic Approximation Method for Nested Stochastic Optimization
Saeed Ghadimi, Andrzej Ruszczyński, Mengdi Wang
We study constrained nested stochastic optimization problems in which the objective function is a composition of two smooth functions whose exact values and derivatives are not ava…
Adaptive Low-Nonnegative-Rank Approximation for State Aggregation of Markov Chains
Yaqi Duan, Mengdi Wang, Zaiwen Wen +1
This paper develops a low-nonnegative-rank approximation method to identify the state aggregation structure of a finite-state Markov chain under an assumption that the state space…
Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model
Aaron Sidford, Mengdi Wang, Xian Wu +2
In this paper we consider the problem of computing an -optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through…
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient
Tianyi Lin, Chenyou Fan, Mengdi Wang +1
Convex composition optimization is an emerging topic that covers a wide range of applications arising from stochastic optimal control, reinforcement learning and multi-stage stocha…
Approximation Methods for Bilevel Programming
Saeed Ghadimi, Mengdi Wang
In this paper, we study a class of bilevel programming problem where the inner objective function is strongly convex. More specifically, under some mile assumptions on the partial…