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Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
We demonstrate that the optimal mistake bound under bandit feedback is at most $O(k)$ times higher than the optimal mistake bound in the full information case, where $k$ represents the number of labels.
Multiclass Online Learnability under Bandit Feedback
We study online multiclass classification under bandit feedback.
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension
We prove an analogous result for randomized learners: we show that the optimal expected mistake bound in learning a class $\mathcal{H}$ equals its randomized Littlestone dimension, which is the largest $d$ for which there exists a tree shattered by $\mathcal{H}$ whose average depth is $2d$.
On Optimal Learning Under Targeted Data Poisoning
In this work we aim to characterize the smallest achievable error $\epsilon=\epsilon(\eta)$ by the learner in the presence of such an adversary in both realizable and agnostic settings.
A Resilient Distributed Boosting Algorithm
Given a learning task where the data is distributed among several parties, communication is one of the fundamental resources which the parties would like to minimize.
