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Creators/Authors contains: "Gopalan, Parikshit"

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  1. ; ; ; (, NeurIPS)
    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 setting or required the activation to be known. Moreover, we only require the marginal to have bounded second moments, whereas all prior work required stronger distributional assumptions (such as anticoncentration or boundedness). Our algorithm is based on recent work by [GHK+23] on omniprediction using predictors satisfying calibrated multiaccuracy. Our analysis is simple and relies on the relationship between Bregman divergences (or matching losses) and ℓp distances. We also provide new guarantees for standard algorithms like GLMtron and logistic regression in the agnostic setting. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, STOC 2023: Proceedings of the 55th Annual ACM Symposium on Theory of Computing)
    Full Text Available 
  3. ; ; ; ; (, 14th Innovations in Theoretical Computer Science Conference (ITCS 2023))
    Accepted Manuscript Full Text Available
  4. ; ; ; (, Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; (, Leibniz international proceedings in informatics)
    Accepted Manuscript Full Text Available
  6. ; ; (, ACM Transactions on Economics and Computation)
    Full Text Available