381 citations · 2.3k across the 89 of their papers we have counts for
6 papers · 2 filters
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications
Daniel Lee, Georgy Noarov, Mallesh Pai +1
We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. Even though the learner'…
Multiaccurate Proxies for Downstream Fairness
Emily Diana, Wesley Gill, Michael Kearns +3
We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time -- in other words, how can we…
Adaptive Machine Unlearning
Varun Gupta, Christopher Jung, Seth Neel +3
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for seq…
Differentially Private Query Release Through Adaptive Projection
Sergul Aydore, William Brown, Michael Kearns +4
We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like -way marginals, subject to differential privacy. Our…
Lexicographically Fair Learning: Algorithms and Generalization
Emily Diana, Wesley Gill, Ira Globus-Harris +3
We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short). Informally, given a…
Online Multivalid Learning: Means, Moments, and Prediction Intervals
Varun Gupta, Christopher Jung, Georgy Noarov +2
We present a general, efficient technique for providing contextual predictions that are "multivalid" in various senses, against an online sequence of adversarially chosen examples…