57 citations · 69 across the 6 of their papers we have counts for
12 papers · 1 filter
Deep k-NN for Noisy Labels
Dara Bahri, Heinrich Jiang, Maya Gupta
Modern machine learning models are often trained on examples with noisy labels that hurt performance and are hard to identify. In this paper, we provide an empirical study showing…
Optimizing Black-box Metrics with Adaptive Surrogates
Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan +2
We address the problem of training models with black-box and hard-to-optimize metrics by expressing the metric as a monotonic function of a small number of easy-to-optimize surroga…
Robust Optimization for Fairness with Noisy Protected Groups
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan +3
Many existing fairness criteria for machine learning involve equalizing some metric across protected groups such as race or gender. However, practitioners trying to audit or enforc…
Deontological Ethics By Monotonicity Shape Constraints
Serena Wang, Maya Gupta
We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not…
Optimizing Generalized Rate Metrics through Game Equilibrium
Harikrishna Narasimhan, Andrew Cotter, Maya Gupta
We present a general framework for solving a large class of learning problems with non-linear functions of classification rates. This includes problems where one wishes to optimize…
Pairwise Fairness for Ranking and Regression
Harikrishna Narasimhan, Andrew Cotter, Maya Gupta +1
We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and sta…