6 papers · 1 filter
Multigroup Fairness and Omniprediction: Separations and Equivalences
Sílvia Casacuberta, Parikshit Gopalan, Varun Kanade +3
Omniprediction is a learning guarantee which requires a single predictor to be competitive relative to the best hypothesis from a benchmark class for any loss chosen from a family…
Efficient Robust Learning at the Information-Theoretic Limit
Adam R. Klivans, Konstantinos Stavropoulos, Sergei Tikhonov +1
In an important recent work, Blanc (2026) gave an algorithm for robustly learning Boolean concept classes with respect to a fixed distribution that outputs a (randomized) classifie…
Iterative Chow Filtering for Learning with Distribution Shift
Gautam Chandrasekaran, Georgios Gkrinias, Adam R. Klivans +2
Recent work due to Goel et al. gave the first efficient algorithms for learning with distribution shift in the challenging PQ framework. In this setting, a learner receives labeled…
Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
Adam R. Klivans, Shyamal Patel, Konstantinos Stavropoulos +1
Recent work on provably efficient algorithms for learning with distribution shift has focused on two models: PQ learning (Goldwasser et al. (2020)) and TDS learning (Klivans et al.…
The Importance of Being Smoothly Calibrated
Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar +1
Recent work has highlighted the centrality of smooth calibration [Kakade and Foster, 2008] as a robust measure of calibration error. We generalize, unify, and extend previous resul…
Sandwiching Polynomials for Geometric Concepts with Low Intrinsic Dimension
Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Recent work has shown the surprising power of low-degree sandwiching polynomial approximators in the context of challenging learning settings such as learning with distribution shi…