150 citations · 554 across the 18 of their papers we have counts for
19 papers
Implicit Rate-Constrained Optimization of Non-decomposable Objectives
Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter
We consider a popular family of constrained optimization problems arising in machine learning that involve optimizing a non-decomposable evaluation metric with a certain thresholde…
Churn Reduction via Distillation
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri +2
In real-world systems, models are frequently updated as more data becomes available, and in addition to achieving high accuracy, the goal is to also maintain a low difference in pr…
Distilling Double Descent
Andrew Cotter, Aditya Krishna Menon, Harikrishna Narasimhan +3
Distillation is the technique of training a "student" model based on examples that are labeled by a separate "teacher" model, which itself is trained on a labeled dataset. The most…
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…
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…