462 citations · 754 across the 15 of their papers we have counts for
15 papers
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium
Chris Junchi Li, Dongruo Zhou, Quanquan Gu +1
We consider learning Nash equilibria in two-player zero-sum Markov Games with nonlinear function approximation, where the action-value function is approximated by a function in a R…
Towards Understanding Mixture of Experts in Deep Learning
Zixiang Chen, Yihe Deng, Yue Wu +2
The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by a router, has achieved great success in deep learning. However, the understanding of such architecture…
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
Jingfeng Wu, Difan Zou, Vladimir Braverman +2
We study linear regression under covariate shift, where the marginal distribution over the input covariates differs in the source and the target domains, while the conditional dist…
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
Jiafan He, Tianhao Wang, Yifei Min +1
We study federated contextual linear bandits, where agents cooperate with each other to solve a global contextual linear bandit problem with the help of a central server. We co…
Learning Neural Contextual Bandits Through Perturbed Rewards
Yiling Jia, Weitong Zhang, Dongruo Zhou +2
Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts. But because…
Benign Overfitting in Adversarially Robust Linear Classification
Jinghui Chen, Yuan Cao, Quanquan Gu
"Benign overfitting", where classifiers memorize noisy training data yet still achieve a good generalization performance, has drawn great attention in the machine learning communit…