Xuezhi Wang

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Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Training and evaluation of fair classifiers is a challenging problem. Th...
02/16/2021 ∙ by Pranjal Awasthi, et al. ∙ 16 ∙ share

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Underspecification Presents Challenges for Credibility in Modern Machine Learning

ML models often exhibit unexpectedly poor behavior when they are deploye...
11/06/2020 ∙ by Alexander D'Amour, et al. ∙ 30 ∙ share

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Measuring and Reducing Gendered Correlations in Pre-trained Models

Pre-trained models have revolutionized natural language understanding. H...
10/12/2020 ∙ by Kellie Webster, et al. ∙ 1 ∙ share

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CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation

NLP models are shown to suffer from robustness issues, i.e., a model's p...
10/05/2020 ∙ by Tianlu Wang, et al. ∙ 0 ∙ share

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ToTTo: A Controlled Table-To-Text Generation Dataset

We present ToTTo, an open-domain English table-to-text dataset with over...
04/29/2020 ∙ by Ankur P. Parikh, et al. ∙ 0 ∙ share

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Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems

Most literature in fairness has focused on improving fairness with respe...
11/05/2019 ∙ by Xuezhi Wang, et al. ∙ 23 ∙ share

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Transfer of Machine Learning Fairness across Domains

If our models are used in new or unexpected cases, do we know if they wi...
06/24/2019 ∙ by Candice Schumann, et al. ∙ 4 ∙ share

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Verifying Text Summaries of Relational Data Sets

We present a novel natural language query interface, the AggChecker, aim...
04/20/2018 ∙ by Saehan Jo, et al. ∙ 0 ∙ share

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Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering

We derive a statistical model for estimation of a dendrogram from single...
11/25/2015 ∙ by Dekang Zhu, et al. ∙ 0 ∙ share

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Statistical Properties of the Single Linkage Hierarchical Clustering Estimator

Distance-based hierarchical clustering (HC) methods are widely used in u...
11/24/2015 ∙ by Dekang Zhu, et al. ∙ 0 ∙ share

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Xuezhi Wang

Featured Co-authors

Xiang Li

Andrea Montanari

Jacob Eisenstein

Mario Lucic

Bhuwan Dhingra

Pranjal Awasthi

Neil Houlsby

Sebastian Gehrmann

Matthew D. Hoffman

Manaal Faruqui

Kang Li

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Underspecification Presents Challenges for Credibility in Modern Machine Learning

Measuring and Reducing Gendered Correlations in Pre-trained Models

CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation

ToTTo: A Controlled Table-To-Text Generation Dataset

Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems

Transfer of Machine Learning Fairness across Domains

Verifying Text Summaries of Relational Data Sets

Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering

Statistical Properties of the Single Linkage Hierarchical Clustering Estimator