1 citations · 1 across the 17 of their papers we have counts for
6 papers · 1 filter
On the ERM Principle in Meta-Learning
Yannay Alon, Steve Hanneke, Shay Moran +1
Classic supervised learning involves algorithms trained on labeled examples to produce a hypothesis aimed at performing well on unseen examples. Meta-learni…
On Reductions and Representations of Learning Problems in Euclidean Spaces
Bogdan Chornomaz, Shay Moran, Tom Waknine
Many practical prediction algorithms represent inputs in Euclidean space and replace the discrete 0/1 classification loss with a real-valued surrogate loss, effectively reducing cl…
Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem
Simone Fioravanti, Steve Hanneke, Shay Moran +2
This work continues to investigate the link between differentially private (DP) and online learning. Alon, Livni, Malliaris, and Moran (2019) showed that for binary concept classes…
Dual VC Dimension Obstructs Sample Compression by Embeddings
Zachary Chase, Bogdan Chornomaz, Steve Hanneke +2
This work studies embedding of arbitrary VC classes in well-behaved VC classes, focusing particularly on extremal classes. Our main result expresses an impossibility: such embeddin…
List Sample Compression and Uniform Convergence
Steve Hanneke, Shay Moran, Tom Waknine
List learning is a variant of supervised classification where the learner outputs multiple plausible labels for each instance rather than just one. We investigate classical princip…
Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
Yuval Filmus, Steve Hanneke, Idan Mehalel +1
Consider the domain of multiclass classification within the adversarial online setting. What is the price of relying on bandit feedback as opposed to full information? To what exte…