4 accepted papers
We study the problem of scalable design of Error-Correcting Output Codes (ECOC) for multi-class classification. Prior works on ECOC-based classifiers are limited to codebooks with small number of rows (classes) or columns, and do not provide optimality guarantees for the codebook design problem. We…
We consider a finite-armed structured bandit problem in which mean rewards of different arms are known functions of a common hidden parameter θ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> . This problem setting subsumes several previously st…
Error-Correcting Output Codes (ECOCs) offer a principled approach for combining binary classifiers into multiclass classifiers. In this paper, we study the problem of designing optimal ECOCs to achieve both nominal and adversarial accuracy using Support Vector Machines (SVMs) and binary deep neural…
Multi-armed bandit models are widely studied sequential decision-making problems that exemplify the exploration-exploitation trade-off. We study a novel correlated multi-armed bandit model where the rewards obtained from the arms are functions of a common latent random variable. We propose and analy…