Scientific practice typically involves repeatedly studying a system, each time trying to unravel
a different perspective. In each study, the scientist may take measurements under different …

We present algorithms for reducing the Dueling Bandits problem to the conventional
(stochastic) Multi-Armed Bandits problem. The Dueling Bandits problem is an online model …

This work establishes distribution-free upper and lower bounds on the minimax label
complexity of active learning with general hypothesis classes, under various noise models …

It has been a long-standing problem to efficiently learn a halfspace using as few labels as
possible in the presence of noise. In this work, we propose an efficient Perceptron-based …

This article studies the achievable guarantees on the error rates of certain learning
algorithms, with particular focus on refining logarithmic factors. Many of the results are based …

Student learners typically engage in an iterative process of actively updating its hypotheses,
like active learning. While this behavior can be advantageous, there is an inherent risk of …

This paper investigates graph clustering in the planted cluster model in the presence of\em
small clusters. Traditional results dictate that for an algorithm to provably correctly recover …

This study presents a novel algorithm which combines active learning (AL) and transfer
learning for medical data classification. The main idea of the proposed algorithm is …

A selective classifier (f, g) comprises a classification function f and a binary selection function
g, which determines if the classifier abstains from prediction, or uses f to predict. The …

Given a graph where vertices represent alternatives and arcs represent pairwise
comparison data, the statistical ranking problem is to find a potential function, defined on the …