We revisit the classical problem of multiclass classification with bandit feedback (Kakade,
Shalev-Shwartz and Tewari, 2008), where each input classifies to one of $ K $ possible …

Bandit online multiclass prediction plays an important role in many real-world applications.
In this paper, we propose a unified framework for it in the fully adversarial setting. This …

In online binary classification under apple tasting feedback, the learner only observes the
true label if it predicts “1”. First studied by Helmbold et al.(2000a), we revisit this classical …

We study the problem of multiclass list classification with (semi-) bandit feedback, where
input examples are mapped into subsets of size $ m $ of a collection of $ K $ possible …

We study multiclass PAC learning with bandit feedback, where inputs are classified into one
of $ K $ possible labels and feedback is limited to whether or not the predicted labels are …

We study the problem of online binary classification in settings where strategic agents can
modify their observable features to receive a positive classification. We model the set of …

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 extent can an …