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20142024
most citedEquality of Opportunity in Supervised Learning

1.9k citations · 3.1k across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.LG2024

Allocation Requires Prediction Only if Inequality Is Low

Ali Shirali, Rediet Abebe, Moritz Hardt

Algorithmic predictions are emerging as a promising solution concept for efficiently allocating societal resources. Fueling their use is an underlying assumption that such systems…

cs.LG2023

Causal Inference out of Control: Estimating the Steerability of Consumption

Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner

Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption. We introduce a general causal inferenc…

cs.LG20161.1k cited

Understanding deep learning requires rethinking generalization

Chiyuan Zhang, Samy Bengio, Moritz Hardt +2

Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attribut…

cs.LG20161.9k cited

Equality of Opportunity in Supervised Learning

Moritz Hardt, Eric Price, Nathan Srebro

We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assu…

cs.LG201423 cited

Preventing False Discovery in Interactive Data Analysis is Hard

Moritz Hardt, Jonathan Ullman

We show that, under a standard hardness assumption, there is no computationally efficient algorithm that given samples from an unknown distribution can give valid answers to $n…

cs.LG201457 cited

Fast matrix completion without the condition number

Moritz Hardt, Mary Wootters

We give the first algorithm for Matrix Completion whose running time and sample complexity is polynomial in the rank of the unknown target matrix, linear in the dimension of the ma…