G Li, Y Wei, Y Chi, Y Gu… - Advances in neural …, 2020 - proceedings.neurips.cc
We investigate the sample efficiency of reinforcement learning in a $\gamma $-discounted infinite-horizon Markov decision process (MDP) with state space S and action space A …
X Xu, Y Shen, Y Chi, C Ma - International Conference on …, 2023 - proceedings.mlr.press
Abstract We propose $\textsf {ScaledGD ($\lambda $)} $, a preconditioned gradient descent method to tackle the low-rank matrix sensing problem when the true rank is unknown, and …
G Li, W Fan, Y Wei - … of the National Academy of Sciences, 2023 - National Acad Sciences
This paper is concerned with the problem of reconstructing an unknown rank-one matrix with prior structural information from noisy observations. While computing the Bayes optimal …
Feature learning is thought to be one of the fundamental reasons for the success of deep neural networks. It is rigorously known that in two-layer fully-connected neural networks …
G Li, Y Wei - arXiv preprint arXiv:2208.03313, 2022 - arxiv.org
Approximate message passing (AMP) emerges as an effective iterative paradigm for solving high-dimensional statistical problems. However, prior AMP theory--which focused mostly on …
This paper delivers improved theoretical guarantees for the convex programming approach in low-rank matrix estimation, in the presence of (1) random noise,(2) gross sparse outliers …
S Stojanovic, Y Jedra… - Advances in Neural …, 2023 - proceedings.neurips.cc
We study matrix estimation problems arising in reinforcement learning with low-rank structure. In low-rank bandits, the matrix to be recovered specifies the expected arm …
Tensors, which provide a powerful and flexible model for representing multi-attribute data and multi-way interactions, play an indispensable role in modern data science across …
This paper makes progress towards learning Nash equilibria in two-player zero-sum Markov games from offline data. Specifically, consider a $\gamma $-discounted infinite-horizon …