3 citations · 11 across the 17 of their papers we have counts for
6 papers · 1 filter
Hidden in the Noise: Two-Stage Robust Watermarking for Images
Kasra Arabi, Benjamin Feuer, R. Teal Witter +2
As the quality of image generators continues to improve, deepfakes become a topic of considerable societal debate. Image watermarking allows responsible model owners to detect and…
FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic Fairness
Lucas Rosenblatt, R. Teal Witter
Fair predictive algorithms hinge on both equality and trust, yet inherent uncertainty in real-world data challenges our ability to make consistent, fair, and calibrated decisions.…
Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values
Yurong Liu, R. Teal Witter, Flip Korn +4
Banzhaf values provide a popular, interpretable alternative to the widely-used Shapley values for quantifying the importance of features in machine learning models. Like Shapley va…
Provably Accurate Shapley Value Estimation via Leverage Score Sampling
Christopher Musco, R. Teal Witter
Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predictions to specific…
Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm
R. Teal Witter, Christopher Musco
Estimating the effect of treatments from natural experiments, where treatments are pre-assigned, is an important and well-studied problem. We introduce a novel natural experiment d…
Minimizing Cost Rather Than Maximizing Reward in Restless Multi-Armed Bandits
R. Teal Witter, Lisa Hellerstein
Restless Multi-Armed Bandits (RMABs) offer a powerful framework for solving resource constrained maximization problems. However, the formulation can be inappropriate for settings w…