
Distributionally Robust Optimization with Markovian Data
We study a stochastic program where the probability distribution of the ...
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Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Training models that perform well under distribution shifts is a central...
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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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SemiDiscrete Optimal Transport: Hardness, Regularization and Numerical Solution
Semidiscrete optimal transport problems, which evaluate the Wasserstein...
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Efficient Learning of a Linear Dynamical System with Stability Guarantees
We propose a principled method for projecting an arbitrary square matrix...
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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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A Distributionally Robust Approach to Fair Classification
We propose a distributionally robust logistic regression model with an u...
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On Linear Optimization over Wasserstein Balls
Wasserstein balls, which contain all probability measures within a pres...
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Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
We introduce a distributionally robust minimium mean square error estima...
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Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
The likelihood function is a fundamental component in Bayesian statistic...
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Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
A fundamental problem arising in many areas of machine learning is the e...
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Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Many decision problems in science, engineering and economics are affecte...
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RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems
Nonlinear optimal control problems are often solved with numerical metho...
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Wasserstein Distributionally Robust Kalman Filtering
We study a distributionally robust mean square error estimation problem ...
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Automatic Exploration of Machine Learning Experiments on OpenML
Understanding the influence of hyperparameters on the performance of a m...
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Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
We introduce a distributionally robust maximum likelihood estimation mod...
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Regularization via Mass Transportation
The goal of regression and classification methods in supervised learning...
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Size Matters: CardinalityConstrained Clustering and Outlier Detection via Conic Optimization
Plain vanilla Kmeans clustering is prone to produce unbalanced clusters...
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Distributionally Robust Logistic Regression
This paper proposes a distributionally robust approach to logistic regre...
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Daniel Kuhn
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