research
∙
08/10/2023
Unifying Distributionally Robust Optimization via Optimal Transport Theory
In the past few years, there has been considerable interest in two promi...
share
research
∙
05/28/2023
Sample Complexity of Variance-reduced Distributionally Robust Q-learning
Dynamic decision making under distributional shifts is of fundamental in...
research
∙
05/25/2023
When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality
This paper studies the use of a machine learning-based estimator as a co...
research
∙
04/11/2023
Neural Network Accelerated Process Design of Polycrystalline Microstructures
Computational experiments are exploited in finding a well-designed proce...
research
∙
03/27/2023
Statistical Limit Theorems in Distributionally Robust Optimization
The goal of this paper is to develop methodology for the systematic anal...
research
∙
03/12/2023
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
In this work, we present the Bregman Alternating Projected Gradient (BAP...
research
∙
02/26/2023
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
We consider a reinforcement learning setting in which the deployment env...
research
∙
02/15/2023
Optimal Sample Complexity of Reinforcement Learning for Uniformly Ergodic Discounted Markov Decision Processes
We consider the optimal sample complexity theory of tabular reinforcemen...
research
∙
01/31/2023
Dynamic Flows on Curved Space Generated by Labeled Data
The scarcity of labeled data is a long-standing challenge for many machi...
research
∙
01/27/2023
Single-Trajectory Distributionally Robust Reinforcement Learning
As a framework for sequential decision-making, Reinforcement Learning (R...
research
∙
12/26/2022
Doubly Smoothed GDA: Global Convergent Algorithm for Constrained Nonconvex-Nonconcave Minimax Optimization
Nonconvex-nonconcave minimax optimization has been the focus of intense ...
research
∙
11/28/2022
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls
The optimal design of experiments typically involves solving an NP-hard ...
research
∙
10/04/2022
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
Distributionally robust optimization has been shown to offer a principle...
research
∙
09/28/2022
Minimax Optimal Kernel Operator Learning via Multilevel Training
Learning mappings between infinite-dimensional function spaces has achie...
research
∙
09/14/2022
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation
Among the reasons hindering reinforcement learning (RL) applications to ...
research
∙
05/17/2022
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Learning
In this paper, we study the design and analysis of a class of efficient ...
research
∙
05/15/2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
In this paper, we study the statistical limits in terms of Sobolev norms...
research
∙
03/14/2022
The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling
Surgical scheduling optimization is an active area of research. However,...
research
∙
02/23/2022
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality
We study the problem of transfer learning, observing that previous effor...
research
∙
02/13/2022
Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data
Using data from cardiovascular surgery patients with long and highly var...
research
∙
02/02/2022
Bayesian Imputation with Optimal Look-Ahead-Bias and Variance Tradeoff
Missing time-series data is a prevalent problem in finance. Imputation m...
research
∙
11/30/2021
Human Imperceptible Attacks and Applications to Improve Fairness
Modern neural networks are able to perform at least as well as humans in...
research
∙
10/13/2021
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
In this paper, we study the statistical limits of deep learning techniqu...
research
∙
09/30/2021
Adversarial Regression with Doubly Non-negative Weighting Matrices
Many machine learning tasks that involve predicting an output response c...
research
∙
08/04/2021
Statistical Analysis of Wasserstein Distributionally Robust Estimators
We consider statistical methods which invoke a min-max distributionally ...
research
∙
06/14/2021
Distributionally Robust Martingale Optimal Transport
We study the problem of bounding path-dependent expectations (within any...
research
∙
06/04/2021
Unbiased Optimal Stopping via the MUSE
We propose a new unbiased estimator for estimating the utility of the op...
research
∙
06/02/2021
Testing Group Fairness via Optimal Transport Projections
We present a statistical testing framework to detect if a given machine ...
research
∙
06/01/2021
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Least squares estimators, when trained on a few target domain samples, m...
research
∙
05/11/2021
Frank-Wolfe Methods in Probability Space
We introduce a new class of Frank-Wolfe algorithms for minimizing differ...
research
∙
03/30/2021
Robustifying Conditional Portfolio Decisions via Optimal Transport
We propose a data-driven portfolio selection model that integrates side ...
research
∙
03/08/2021
No Discounted-Regret Learning in Adversarial Bandits with Delays
Consider a player that in each round t out of T rounds chooses an action...
research
∙
02/25/2021
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff
Missing time-series data is a prevalent practical problem. Imputation me...
research
∙
12/09/2020
A Statistical Test for Probabilistic Fairness
Algorithms are now routinely used to make consequential decisions that a...
research
∙
10/12/2020
Distributionally Robust Local Non-parametric Conditional Estimation
Conditional estimation given specific covariate values (i.e., local cond...
research
∙
10/11/2020
Distributionally Robust Parametric Maximum Likelihood Estimation
We consider the parameter estimation problem of a probabilistic generati...
research
∙
09/13/2020
Machine Learning's Dropout Training is Distributionally Robust Optimal
This paper shows that dropout training in Generalized Linear Models is t...
research
∙
07/18/2020
A Distributionally Robust Approach to Fair Classification
We propose a distributionally robust logistic regression model with an u...
research
∙
07/08/2020
Robust Bayesian Classification Using an Optimistic Score Ratio
We build a Bayesian contextual classification model using an optimistic ...
research
∙
06/10/2020
Distributional Robust Batch Contextual Bandits
Policy learning using historical observational data is an important prob...
research
∙
04/14/2020
Sequential Batch Learning in Finite-Action Linear Contextual Bandits
We study the sequential batch learning problem in linear contextual band...
research
∙
03/11/2020
Delay-Adaptive Learning in Generalized Linear Contextual Bandits
In this paper, we consider online learning in generalized linear context...
research
∙
06/07/2019
Optimal Transport Relaxations with Application to Wasserstein GANs
We propose a family of relaxations of the optimal transport problem whic...
research
∙
06/04/2019
Confidence Regions in Wasserstein Distributionally Robust Estimation
Wasserstein distributionally robust optimization (DRO) estimators are ob...
research
∙
05/29/2019
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss
This paper proposes a novel non-parametric multidimensional convex regre...
research
∙
05/20/2019
A Distributionally Robust Boosting Algorithm
Distributionally Robust Optimization (DRO) has been shown to provide a f...
research
∙
01/30/2019
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning
The goal of this paper is to provide a unifying view of a wide range of ...
research
∙
01/23/2019
Optimal Uncertainty Size in Distributionally Robust Inverse Covariance Estimation
In a recent paper, Nguyen, Kuhn, and Esfahani (2018) built a distributio...
research
∙
01/07/2019
Semi-parametric dynamic contextual pricing
We consider a canonical revenue maximization problem where customers arr...
research
∙
10/17/2018