30 citations · 138 across the 35 of their papers we have counts for
13 papers · 1 filter
Tight Bounds for Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function
Anh Tuan Nguyen, Viet Anh Nguyen
Data-driven algorithm design frames hyperparameter tuning as a statistical learning problem, but establishing generalization guarantees remains challenging due to the implicit, non…
Efficient Failure Pattern Identification of Predictive Algorithms
Bao Nguyen, Viet Anh Nguyen
Given a (machine learning) classifier and a collection of unlabeled data, how can we efficiently identify misclassification patterns presented in this dataset? To address this prob…
Dynamic Flows on Curved Space Generated by Labeled Data
Xinru Hua, Truyen Nguyen, Tam Le +2
The scarcity of labeled data is a long-standing challenge for many machine learning tasks. We propose our gradient flow method to leverage the existing dataset (i.e., source) to ge…
Robust Bayesian Recourse
Tuan-Duy H. Nguyen, Ngoc Bui, Duy Nguyen +2
Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a model-agnos…
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
Tam Le, Truyen Nguyen, Dinh Phung +1
Optimal transport (OT) is a popular measure to compare probability distributions. However, OT suffers a few drawbacks such as (i) a high complexity for computation, (ii) indefinite…
Distributionally Robust Fair Principal Components via Geodesic Descents
Hieu Vu, Toan Tran, Man-Chung Yue +1
Principal component analysis is a simple yet useful dimensionality reduction technique in modern machine learning pipelines. In consequential domains such as college admission, hea…
Counterfactual Plans under Distributional Ambiguity
Ngoc Bui, Duy Nguyen, Viet Anh Nguyen
Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains. A counterfactual plan cons…
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man-Chung Yue, Jose Blanchet +2
Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additi…
Wasserstein Robust Classification with Fairness Constraints
Yijie Wang, Viet Anh Nguyen, Grani A. Hanasusanto
We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion. We…
A Statistical Test for Probabilistic Fairness
Bahar Taskesen, Jose Blanchet, Daniel Kuhn +1
Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algo…
A Distributionally Robust Approach to Fair Classification
Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn +1
We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnic…
Robust Bayesian Classification Using an Optimistic Score Ratio
Viet Anh Nguyen, Nian Si, Jose Blanchet
We build a Bayesian contextual classification model using an optimistic score ratio for robust binary classification when there is limited information on the class-conditional, or…
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue +2
The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications.…