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Found 7 papers, 2 papers with code
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning
There, unlike in classical crowdsourced ML, participants deliberately specialize their efforts by working on subproblems, such as demographic subgroups in the service of fairness.
Multicalibration as Boosting for Regression
Using this characterization, we give an exceedingly simple algorithm that can be analyzed both as a boosting algorithm for regression and as a multicalibration algorithm for a class H that makes use only of a standard squared error regression oracle for H. We give a weak learning assumption on H that ensures convergence to Bayes optimality without the need to make any realizability assumptions -- giving us an agnostic boosting algorithm for regression.
Multicalibrated Regression for Downstream Fairness
We show how to take a regression function $\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints.
An Algorithmic Framework for Bias Bounties
We propose and analyze an algorithmic framework for "bias bounties": events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security.
Non-parametric Differentially Private Confidence Intervals for the Median
Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data.
Lexicographically Fair Learning: Algorithms and Generalization
We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short).
Improved Differentially Private Analysis of Variance
Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines.
