In this work, we aim to characterize the statistical complexity of realizable regression both in
the PAC learning setting and the online learning setting. Previous work had established the …

We study online multiclass classification under bandit feedback. We extend the results of
Daniely and Helbertal [2013] by showing that the finiteness of the Bandit Littlestone …

We study a variant of online multiclass classification where the learner predicts a single
label but receives a\textit {set of labels} as feedback. In this model, the learner is penalized …

This article presents a number of elementary observations and relations concerning
commonly-studied combinatorial dimensions from the learning theory literature on …

We consider the problem of learning linear operators under squared loss between two
infinite-dimensional Hilbert spaces in the online setting. We show that the class of linear …

Multilabel ranking is a central task in machine learning. However, the most fundamental
question of learnability in a multilabel ranking setting with relevance-score feedback …

We study the online learnability of hypothesis classes with respect to arbitrary, but bounded,
loss functions. We give a new scale-sensitive combinatorial dimension, named the …

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 …

We consider the problem of learning linear operators under squared loss between two
infinite-dimensional Hilbert spaces in the online setting. We show that the class of linear …

We study online classification when the learner has access to predictions about future
examples. We design an online learner whose expected regret is never worse than the …