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stat.ML2026
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…
stat.ML2020★ 2 cited
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…