13 citations · 14 across the 5 of their papers we have counts for
5 papers
An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning
Xingtu Liu
In this work, we study out-of-distribution (OOD) generalization in meta-reinforcement learning from an information-theoretic perspective. We begin by establishing OOD generalizatio…
Central Limit Theorems for Asynchronous Averaged Q-Learning
Xingtu Liu
This paper establishes central limit theorems for Polyak-Ruppert averaged Q-learning under asynchronous updates. We prove a non-asymptotic central limit theorem, where the converge…
Sample Complexity Bounds for Linear Constrained MDPs with a Generative Model
Xingtu Liu, Lin F. Yang, Sharan Vaswani
We consider infinite-horizon -discounted (linear) constrained Markov decision processes (CMDPs) where the objective is to find a policy that maximizes the expected cumulative re…
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
Gautam Kamath, Xingtu Liu, Huanyu Zhang
We study stochastic convex optimization with heavy-tailed data under the constraint of differential privacy (DP). Most prior work on this problem is restricted to the case where th…
Neural Networks with Complex-Valued Weights Have No Spurious Local Minima
Xingtu Liu
We study the benefits of complex-valued weights for neural networks. We prove that shallow complex neural networks with quadratic activations have no spurious local minima. In cont…