10 citations · 43 across the 12 of their papers we have counts for
10 papers · 1 filter
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
Gautam Chandrasekaran, Vasilis Kontonis, Konstantinos Stavropoulos +1
We study the problem of PAC learning -margin halfspaces with Massart noise. We propose a simple proper learning algorithm, the Perspectron, that has sample complexity $\widetild…
Weighted Distillation with Unlabeled Examples
Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal +3
Distillation with unlabeled examples is a popular and powerful method for training deep neural networks in settings where the amount of labeled data is limited: A large ''teacher''…
Learning General Halfspaces with General Massart Noise under the Gaussian Distribution
Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis +2
We study the problem of PAC learning halfspaces on with Massart noise under the Gaussian distribution. In the Massart model, an adversary is allowed to flip the labe…
Agnostic Proper Learning of Halfspaces under Gaussian Marginals
Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis +2
We study the problem of agnostically learning halfspaces under the Gaussian distribution. Our main result is the {\em first proper} learning algorithm for this problem whose sample…
Convergence and Sample Complexity of SGD in GANs
Vasilis Kontonis, Sihan Liu, Christos Tzamos
We provide theoretical convergence guarantees on training Generative Adversarial Networks (GANs) via SGD. We consider learning a target distribution modeled by a 1-layer Generator…
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…