24 citations · 87 across the 36 of their papers we have counts for
4 papers · 1 filter
Agnostically Learning Single-Index Models using Omnipredictors
Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans +1
We give the first result for agnostically learning Single-Index Models (SIMs) with arbitrary monotone and Lipschitz activations. All prior work either held only in the realizable s…
When Does Optimizing a Proper Loss Yield Calibration?
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu +1
Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to pred…
Loss Minimization Yields Multicalibration for Large Neural Networks
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu +2
Multicalibration is a notion of fairness for predictors that requires them to provide calibrated predictions across a large set of protected groups. Multicalibration is known to be…
Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
Parikshit Gopalan, Michael P. Kim, Omer Reingold
We introduce and study Swap Agnostic Learning. The problem can be phrased as a game between a predictor and an adversary: first, the predictor selects a hypothesis ; then, the a…