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We give the first result for agnostically learning Single-Index Models (SIMs) with arbitrary monotone and Lipschitz activations.
Generalized linear models
Peter McCullagh · 1984
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Efficient distribution-free learning of probabilistic concepts
Michael J Kearns and Robert E Schapire · 1994
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Exponentially many local minima for single neurons
Peter Auer, Mark Herbster, and Manfred K. K Warmuth · 1995
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The chow parameters problem
Ryan O’Donnell and Rocco A Servedio · 2008
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The isotron algorithm: High-dimensional isotonic regression
Adam Tauman Kalai and Ravi Sastry · 2009
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Efficient learning of generalized linear and single index models with isotonic regression
Sham M Kakade, Varun Kanade, Ohad Shamir, and Adam Kalai · 2011
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Learning kernel-based halfspaces with the 0-1 loss
Shai Shalev-Shwartz, Ohad Shamir, and Karthik Sridharan · 2011
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Foundations of linear and generalized linear models
Alan Agresti · 2015
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Reliably learning the relu in polynomial time
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Agnostic learning of a single neuron with gradient descent
Spencer Frei, Yuan Cao, and Quanquan Gu · 2020
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Statistical-query lower bounds via functional gradients
Surbhi Goel, Aravind Gollakota, and Adam Klivans · 2020
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The optimality of polynomial regression for agnostic learning under gaussian marginals in the sq model
Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, and Nikos Zarifis · 2021
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Hardness of learning a single neuron with adversarial label noise
Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, and Lisheng Ren · 2022
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Learning a single neuron with adversarial label noise via gradient descent
Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, and Nikos Zarifis · 2022
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Omnipredictors
Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, and Udi Wieder · 2022
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Non-convex sgd learns halfspaces with adversarial label noise
Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, and Nikos Zarifis · 2020
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Near-optimal sq lower bounds for agnostically learning halfspaces and relus under gaussian marginals
Ilias Diakonikolas, Daniel Kane, and Nikos Zarifis · 2020
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Loss minimization through the lens of outcome indistinguishability
Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, and Udi Wieder · 2023
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