Search Results for author: Xuhui Zhang

Found 5 papers, 1 papers with code

A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality

no code implementations23 Feb 2022 Xuhui Zhang, Jose Blanchet, Soumyadip Ghosh, Mark S. Squillante

In contrast, our study first illustrates the benefits of incorporating a natural geometric structure within a linear regression model, which corresponds to the generalized eigenvalue problem formed by the Gram matrices of both domains.

Transfer Learning

Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff

no code implementations25 Feb 2021 Jose Blanchet, Fernando Hernandez, Viet Anh Nguyen, Markus Pelger, Xuhui Zhang

Imputation methods in time-series data often are applied to the full panel data with the purpose of training a model for a downstream out-of-sample task.

Imputation Portfolio Optimization +2

Distributionally Robust Parametric Maximum Likelihood Estimation

1 code implementation NeurIPS 2020 Viet Anh Nguyen, Xuhui Zhang, Jose Blanchet, Angelos Georghiou

We consider the parameter estimation problem of a probabilistic generative model prescribed using a natural exponential family of distributions.

Machine Learning's Dropout Training is Distributionally Robust Optimal

no code implementations13 Sep 2020 Jose Blanchet, Yang Kang, Jose Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang

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 covariates using a multiplicative nonparametric errors-in-variables model.

Latent Variable Discovery Using Dependency Patterns

no code implementations22 Jul 2016 Xuhui Zhang, Kevin B. Korb, Ann E. Nicholson, Steven Mascaro

The causal discovery of Bayesian networks is an active and important research area, and it is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data.

Causal Discovery

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