
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...
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Adversarial Regression with Doubly Nonnegative Weighting Matrices
Many machine learning tasks that involve predicting an output response c...
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Statistical Analysis of Wasserstein Distributionally Robust Estimators
We consider statistical methods which invoke a minmax distributionally ...
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Distributionally Robust Martingale Optimal Transport
We study the problem of bounding pathdependent expectations (within any...
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Unbiased Optimal Stopping via the MUSE
We propose a new unbiased estimator for estimating the utility of the op...
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Testing Group Fairness via Optimal Transport Projections
We present a statistical testing framework to detect if a given machine ...
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Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Least squares estimators, when trained on a few target domain samples, m...
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FrankWolfe Methods in Probability Space
We introduce a new class of FrankWolfe algorithms for minimizing differ...
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Robustifying Conditional Portfolio Decisions via Optimal Transport
We propose a datadriven portfolio selection model that integrates side ...
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No DiscountedRegret Learning in Adversarial Bandits with Delays
Consider a player that in each round t out of T rounds chooses an action...
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TimeSeries Imputation with Wasserstein Interpolation for Optimal LookAheadBias and Variance Tradeoff
Missing timeseries data is a prevalent practical problem. Imputation me...
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A Statistical Test for Probabilistic Fairness
Algorithms are now routinely used to make consequential decisions that a...
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Distributionally Robust Local Nonparametric Conditional Estimation
Conditional estimation given specific covariate values (i.e., local cond...
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Distributionally Robust Parametric Maximum Likelihood Estimation
We consider the parameter estimation problem of a probabilistic generati...
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Machine Learning's Dropout Training is Distributionally Robust Optimal
This paper shows that dropout training in Generalized Linear Models is t...
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A Distributionally Robust Approach to Fair Classification
We propose a distributionally robust logistic regression model with an u...
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Robust Bayesian Classification Using an Optimistic Score Ratio
We build a Bayesian contextual classification model using an optimistic ...
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Distributional Robust Batch Contextual Bandits
Policy learning using historical observational data is an important prob...
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Sequential Batch Learning in FiniteAction Linear Contextual Bandits
We study the sequential batch learning problem in linear contextual band...
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DelayAdaptive Learning in Generalized Linear Contextual Bandits
In this paper, we consider online learning in generalized linear context...
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Optimal Transport Relaxations with Application to Wasserstein GANs
We propose a family of relaxations of the optimal transport problem whic...
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Confidence Regions in Wasserstein Distributionally Robust Estimation
Wasserstein distributionally robust optimization (DRO) estimators are ob...
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Multivariate Distributionally Robust Convex Regression under Absolute Error Loss
This paper proposes a novel nonparametric multidimensional convex regre...
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A Distributionally Robust Boosting Algorithm
Distributionally Robust Optimization (DRO) has been shown to provide a f...
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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 ...
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Optimal Uncertainty Size in Distributionally Robust Inverse Covariance Estimation
In a recent paper, Nguyen, Kuhn, and Esfahani (2018) built a distributio...
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Semiparametric dynamic contextual pricing
We consider a canonical revenue maximization problem where customers arr...
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Towards Optimal Running Times for Optimal Transport
In this work, we provide faster algorithms for approximating the optimal...
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Bandit Learning with Positive Externalities
Many platforms are characterized by the fact that future user arrivals a...
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Distributionally Robust MeanVariance Portfolio Selection with Wasserstein Distances
We revisit Markowitz's meanvariance portfolio selection model by consid...
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Unbiased Simulation for Optimizing Stochastic Function Compositions
In this paper, we introduce an unbiased gradient simulation algorithms f...
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Doubly Robust DataDriven Distributionally Robust Optimization
Datadriven Distributionally Robust Optimization (DDDRO) via optimal tr...
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Datadriven Optimal Transport Cost Selection for Distributionally Robust Optimizatio
Recently, (Blanchet, Kang, and Murhy 2016) showed that several machine l...
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Semisupervised Learning based on Distributionally Robust Optimization
We propose a novel method for semisupervised learning (SSL) based on da...
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Jose Blanchet
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