
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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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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Wasserstein Distributionally Robust Kalman Filtering
We study a distributionally robust mean square error estimation problem ...
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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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Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier
Total variation has proved its effectiveness in solving inverse problems...
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Weakly Supervised FineGrained Image Categorization
In this paper, we categorize finegrained images without using any objec...
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Viet Anh Nguyen
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