9 citations · 17 across the 10 of their papers we have counts for
10 papers
Distributionally Robust Optimization and Robust Statistics
Jose Blanchet, Jiajin Li, Sirui Lin +1
We review distributionally robust optimization (DRO), a principled approach for constructing statistical estimators that hedge against the impact of deviations in the expected loss…
Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes
Xuhui Zhang, Jose Blanchet, Youssef Marzouk +2
We present the first minimax risk bounds for estimators of the spectral measure in multivariate linear factor models, where observations are linear combinations of regularly varyin…
Distributionally Robust Gaussian Process Regression and Bayesian Inverse Problems
Xuhui Zhang, Jose Blanchet, Youssef Marzouk +2
We study a distributionally robust optimization formulation (i.e., a min-max game) for two representative problems in Bayesian nonparametric estimation: Gaussian process regression…
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…
Bayesian Imputation with Optimal Look-Ahead-Bias and Variance Tradeoff
Jose Blanchet, Fernando Hernandez, Viet Anh Nguyen +2
Missing time-series data is a prevalent problem in many prescriptive analytics models in operations management, healthcare and finance. Imputation methods for time-series data are…
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff
Jose Blanchet, Fernando Hernandez, Viet Anh Nguyen +2
Missing time-series data is a prevalent practical problem. Imputation methods in time-series data often are applied to the full panel data with the purpose of training a model for…
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…