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02/02/2022
Global Optimization Networks
We consider the problem of estimating a good maximizer of a black-box fu...
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02/09/2021
Regularization Strategies for Quantile Regression
We investigate different methods for regularizing quantile regression wh...
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04/26/2020
Deep k-NN for Noisy Labels
Modern machine learning models are often trained on examples with noisy ...
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02/21/2020
Robust Optimization for Fairness with Noisy Protected Groups
Many existing fairness criteria for machine learning involve equalizing ...
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02/20/2020
Optimizing Black-box Metrics with Adaptive Surrogates
We address the problem of training models with black-box and hard-to-opt...
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01/31/2020
Deontological Ethics By Monotonicity Shape Constraints
We demonstrate how easy it is for modern machine-learned systems to viol...
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09/06/2019
Optimizing Generalized Rate Metrics through Game Equilibrium
We present a general framework for solving a large class of learning pro...
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06/12/2019
Pairwise Fairness for Ranking and Regression
We present pairwise metrics of fairness for ranking and regression model...
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05/31/2019
Minimum-Margin Active Learning
We present a new active sampling method we call min-margin which trains ...
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09/11/2018
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
We show that many machine learning goals, such as improved fairness metr...
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06/29/2018
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Classifiers can be trained with data-dependent constraints to satisfy fa...
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06/28/2018
Proxy Fairness
We consider the problem of improving fairness when one lacks access to a...
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06/28/2018
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization
Given a classifier ensemble and a set of examples to be classified, many...
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05/31/2018
Interpretable Set Functions
We propose learning flexible but interpretable functions that aggregate ...
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05/30/2018
To Trust Or Not To Trust A Classifier
Knowing when a classifier's prediction can be trusted is useful in many ...
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05/27/2018
Metric-Optimized Example Weights
Real-world machine learning applications often have complex test metrics...
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09/19/2017
Deep Lattice Networks and Partial Monotonic Functions
We propose learning deep models that are monotonic with respect to a use...
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06/18/2012