278 citations · 670 across the 20 of their papers we have counts for
20 papers
Global Optimization Networks
Sen Zhao, Erez Louidor, Olexander Mangylov +1
We consider the problem of estimating a good maximizer of a black-box function given noisy examples. To solve such problems, we propose to fit a new type of function which we call…
Regularization Strategies for Quantile Regression
Taman Narayan, Serena Wang, Kevin Canini +1
We investigate different methods for regularizing quantile regression when predicting either a subset of quantiles or the full inverse CDF. We show that minimizing an expected pinb…
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…