7 citations · 10 across the 21 of their papers we have counts for
5 papers · 1 filter
A Trichotomy for Transductive Online Learning
Steve Hanneke, Shay Moran, Jonathan Shafer
We present new upper and lower bounds on the number of learner mistakes in the `transductive' online learning setting of Ben-David, Kushilevitz and Mansour (1997). This setting is…
Universal Rates for Multiclass Learning
Steve Hanneke, Shay Moran, Qian Zhang
We study universal rates for multiclass classification, establishing the optimal rates (up to log factors) for all hypothesis classes. This generalizes previous results on binary c…
Adversarial Resilience in Sequential Prediction via Abstention
Surbhi Goel, Steve Hanneke, Shay Moran +1
We study the problem of sequential prediction in the stochastic setting with an adversary that is allowed to inject clean-label adversarial (or out-of-distribution) examples. Algor…
Multiclass Online Learning and Uniform Convergence
Steve Hanneke, Shay Moran, Vinod Raman +2
We study multiclass classification in the agnostic adversarial online learning setting. As our main result, we prove that any multiclass concept class is agnostically learnable if…
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension
Yuval Filmus, Steve Hanneke, Idan Mehalel +1
A classical result in online learning characterizes the optimal mistake bound achievable by deterministic learners using the Littlestone dimension (Littlestone '88). We prove an an…