3 citations · 6 across the 11 of their papers we have counts for
11 papers · 1 filter
Accuracy vs. Accuracy: Computational Tradeoffs Between Classification Rates and Utility
Noga Amit, Omer Reingold, Guy N. Rothblum
We revisit the foundations of fairness and its interplay with utility and efficiency in settings where the training data contain richer labels, such as individual types, rankings,…
Models That Prove Their Own Correctness
Noga Amit, Shafi Goldwasser, Orr Paradise +1
How can we trust the correctness of a learned model on a particular input of interest? Model accuracy is typically measured on average over a distribution of inputs, giving no guar…
On Computationally Efficient Multi-Class Calibration
Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum
Consider a multi-class labelling problem, where the labels can take values in , and a predictor predicts a distribution over the labels. In this work, we study the following f…
Decision-Making under Miscalibration
Guy N. Rothblum, Gal Yona
ML-based predictions are used to inform consequential decisions about individuals. How should we use predictions (e.g., risk of heart attack) to inform downstream binary classifica…
Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification
Guy N. Rothblum, Gal Yona
In many machine learning settings there is an inherent tension between fairness and accuracy desiderata. How should one proceed in light of such trade-offs? In this work we introdu…
Multi-group Agnostic PAC Learnability
Guy N Rothblum, Gal Yona
An agnostic PAC learning algorithm finds a predictor that is competitive with the best predictor in a benchmark hypothesis class, where competitiveness is measured with respect to…