70 citations · 880 across the 62 of their papers we have counts for
13 papers · 1 filter
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…
State Aggregation Learning from Markov Transition Data
Yaqi Duan, Zheng Tracy Ke, Mengdi Wang
State aggregation is a popular model reduction method rooted in optimal control. It reduces the complexity of engineering systems by mapping the system's states into a small number…
Towards Coherent and Cohesive Long-form Text Generation
Woon Sang Cho, Pengchuan Zhang, Yizhe Zhang +5
Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, fe…
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…