Separable Bandit Classification
This repository contains source files of the paper titled Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case authored by Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei and Chicheng Zhang. The paper was accepted at ICML 2019.
Bandit multiclass classification is a problem where in each round an algorithm receives a feature vector and it needs to predict one of K classes, after which it receives binary feedback whether or not the class was correct. We design efficient algorithms under the assumption that data are linearly separable.