2 citations · 2 across the 5 of their papers we have counts for
5 papers
PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting
Steve Hanneke, Qinglin Meng, Shay Moran +1
We study the problem of multiclass PAC learning with bandit feedback in the realizable setting. In this framework, there is an unknown data distribution over an instance space $\ma…
An Optimal Sauer Lemma Over -ary Alphabets
Steve Hanneke, Qinglin Meng, Shay Moran +1
The Sauer-Shelah-Perles Lemma is a cornerstone of combinatorics and learning theory, bounding the size of a binary hypothesis class in terms of its Vapnik-Chervonenkis (VC) dimensi…
Multiclass Transductive Online Learning
Steve Hanneke, Vinod Raman, Amirreza Shaeiri +1
We consider the problem of multiclass transductive online learning when the number of labels can be unbounded. Previous works by Ben-David et al. [1997] and Hanneke et al. [2023b]…
Towards Deep Learning Models Resistant to Large Perturbations
Amirreza Shaeiri, Rozhin Nobahari, Mohammad Hossein Rohban
Adversarial robustness has proven to be a required property of machine learning algorithms. A key and often overlooked aspect of this problem is to try to make the adversarial nois…
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection
Mohammadreza Salehi, Atrin Arya, Barbod Pajoum +4
Autoencoders (AE) have recently been widely employed to approach the novelty detection problem. Trained only on the normal data, the AE is expected to reconstruct the normal data e…