43 citations · 83 across the 8 of their papers we have counts for
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cs.LG2020
On the Complexity of Learning from Label Proportions
Benjamin Fish, Lev Reyzin
In the problem of learning with label proportions, which we call LLP learning, the training data is unlabeled, and only the proportions of examples receiving each label are given.…
cs.LG2017★ 2 cited
Sampling Without Compromising Accuracy in Adaptive Data Analysis
Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein
In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing the accuracy of those mechanisms. This is important…
cs.LG2016
A Confidence-Based Approach for Balancing Fairness and Accuracy
Benjamin Fish, Jeremy Kun, Ádám D. Lelkes
We study three classical machine learning algorithms in the context of algorithmic fairness: adaptive boosting, support vector machines, and logistic regression. Our goal is to mai…