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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...
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Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding. H...
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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...
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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...
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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...
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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...
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Verifying Text Summaries of Relational Data Sets
We present a novel natural language query interface, the AggChecker, aim...
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Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering
We derive a statistical model for estimation of a dendrogram from single...
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Statistical Properties of the Single Linkage Hierarchical Clustering Estimator
Distance-based hierarchical clustering (HC) methods are widely used in u...
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