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06/01/2023
Efficient Failure Pattern Identification of Predictive Algorithms
Given a (machine learning) classifier and a collection of unlabeled data...
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02/22/2023
Feasible Recourse Plan via Diverse Interpolation
Explaining algorithmic decisions and recommending actionable feedback is...
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02/22/2023
Distributionally Robust Recourse Action
A recourse action aims to explain a particular algorithmic decision by s...
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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...
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11/08/2022
Improving performance of real-time full-band blind packet-loss concealment with predictive network
Packet loss concealment (PLC) is a tool for enhancing speech degradation...
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10/04/2022
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
Distributionally robust optimization has been shown to offer a principle...
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08/16/2022
A Deep Reinforcement Learning-based Adaptive Charging Policy for Wireless Rechargeable Sensor Networks
Wireless sensor networks consist of randomly distributed sensor nodes fo...
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06/22/2022
Robust Bayesian Recourse
Algorithmic recourse aims to recommend an informative feedback to overtu...
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02/22/2022
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
Optimal transport (OT) is a popular measure to compare probability distr...
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02/07/2022
Distributionally Robust Fair Principal Components via Geodesic Descents
Principal component analysis is a simple yet useful dimensionality reduc...
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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...
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01/29/2022
Counterfactual Plans under Distributional Ambiguity
Counterfactual explanations are attracting significant attention due to ...
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10/26/2021
TUNet: A Block-online Bandwidth Extension Model based on Transformers and Self-supervised Pretraining
We introduce a block-online variant of the temporal feature-wise linear ...
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09/30/2021
Adversarial Regression with Doubly Non-negative Weighting Matrices
Many machine learning tasks that involve predicting an output response c...
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08/04/2021
Statistical Analysis of Wasserstein Distributionally Robust Estimators
We consider statistical methods which invoke a min-max distributionally ...
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06/02/2021
Testing Group Fairness via Optimal Transport Projections
We present a statistical testing framework to detect if a given machine ...
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06/01/2021
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Least squares estimators, when trained on a few target domain samples, m...
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05/25/2021
Principal Component Hierarchy for Sparse Quadratic Programs
We propose a novel approximation hierarchy for cardinality-constrained, ...
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03/30/2021
Robustifying Conditional Portfolio Decisions via Optimal Transport
We propose a data-driven portfolio selection model that integrates side ...
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03/11/2021
Wasserstein Robust Support Vector Machines with Fairness Constraints
We propose a distributionally robust support vector machine with a fairn...
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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...
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02/24/2021
From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection
Natural language processing is a fast-growing field of artificial intell...
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12/09/2020
A Statistical Test for Probabilistic Fairness
Algorithms are now routinely used to make consequential decisions that a...
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10/12/2020
Distributionally Robust Local Non-parametric Conditional Estimation
Conditional estimation given specific covariate values (i.e., local cond...
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10/11/2020
Distributionally Robust Parametric Maximum Likelihood Estimation
We consider the parameter estimation problem of a probabilistic generati...
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09/13/2020
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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07/18/2020
A Distributionally Robust Approach to Fair Classification
We propose a distributionally robust logistic regression model with an u...
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07/08/2020
Robust Bayesian Classification Using an Optimistic Score Ratio
We build a Bayesian contextual classification model using an optimistic ...
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11/08/2019
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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10/23/2019
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
The likelihood function is a fundamental component in Bayesian statistic...
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10/17/2019
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
A fundamental problem arising in many areas of machine learning is the e...
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08/23/2019
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Many decision problems in science, engineering and economics are affecte...
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09/24/2018
Wasserstein Distributionally Robust Kalman Filtering
We study a distributionally robust mean square error estimation problem ...
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05/18/2018
Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
We introduce a distributionally robust maximum likelihood estimation mod...
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08/28/2016
Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier
Total variation has proved its effectiveness in solving inverse problems...
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04/20/2015