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20172020
most citedAlgorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks

10 citations · 37 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG20209 cited

A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise

Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis +2

We study the problem of PAC learning homogeneous halfspaces in the presence of Tsybakov noise. In the Tsybakov noise model, the label of every sample is independently flipped with…

cs.LG202010 cited

Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks

Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis +1

We study the problem of PAC learning one-hidden-layer ReLU networks with hidden units on under Gaussian marginals in the presence of additive label noise. For th…

cs.LG20204 cited

Non-Convex SGD Learns Halfspaces with Adversarial Label Noise

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos +1

We study the problem of agnostically learning homogeneous halfspaces in the distribution-specific PAC model. For a broad family of structured distributions, including log-concave d…

cs.LG20207 cited

Learning Halfspaces with Tsybakov Noise

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos +1

We study the efficient PAC learnability of halfspaces in the presence of Tsybakov noise. In the Tsybakov noise model, each label is independently flipped with some probability whic…

cs.LG20207 cited

Learning Halfspaces with Massart Noise Under Structured Distributions

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos +1

We study the problem of learning halfspaces with Massart noise in the distribution-specific PAC model. We give the first computationally efficient algorithm for this problem with r…

math.ST2019

Efficient Truncated Statistics with Unknown Truncation

Vasilis Kontonis, Christos Tzamos, Manolis Zampetakis

We study the problem of estimating the parameters of a Gaussian distribution when samples are only shown if they fall in some (unknown) subset . This core problem…