2 citations · 3 across the 2 of their papers we have counts for
5 papers
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality
Xuhui Zhang, Jose Blanchet, Soumyadip Ghosh +1
We study the problem of transfer learning, observing that previous efforts to understand its information-theoretic limits do not fully exploit the geometric structure of the source…
Distributionally Robust Parametric Maximum Likelihood Estimation
Viet Anh Nguyen, Xuhui Zhang, Jose Blanchet +1
We consider the parameter estimation problem of a probabilistic generative model prescribed using a natural exponential family of distributions. For this problem, the typical maxim…
Machine Learning's Dropout Training is Distributionally Robust Optimal
Jose Blanchet, Yang Kang, Jose Luis Montiel Olea +2
This paper shows that dropout training in Generalized Linear Models is the minimax solution of a two-player, zero-sum game where an adversarial nature corrupts a statistician's cov…
Minimax Efficient Finite-Difference Stochastic Gradient Estimators Using Black-Box Function Evaluations
Henry Lam, Haidong Li, Xuhui Zhang
Standard approaches to stochastic gradient estimation, with only noisy black-box function evaluations, use the finite-difference method or its variants. While natural, it is open t…
Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective
Henry Lam, Xinyu Zhang, Xuhui Zhang
Biased stochastic estimators, such as finite-differences for noisy gradient estimation, often contain parameters that need to be properly chosen to balance impacts from the bias an…