We introduce a novel framework for classification with a rejection option that consists of
simultaneously learning two functions: a classifier along with a rejection function. We …

Learning with abstention is a key scenario where the learner can abstain from making a
prediction at some cost. In this paper, we analyze the score-based formulation of learning …

We study the key framework of learning with abstention in the multi-class classification
setting. In this setting, the learner can choose to abstain from making a prediction with some …

We present a new boosting algorithm for the key scenario of binary classification with
abstention where the algorithm can abstain from predicting the label of a point, at the price of …

We introduce a novel framework for classification with a rejection option that consists of
simultaneously learning two functions: a classifier along with a rejection function. We …

Does everyone equally benefit from computer vision systems? Answers to this question
become more and more important as computer vision systems are deployed at large scale …

We study the problem of sequential prediction in the stochastic setting with an adversary that
is allowed to inject clean-label adversarial (or out-of-distribution) examples. Algorithms …

In many real-world settings exact perturbation sets to be used by an adversary are not
plausibly available to a learner. While prior literature has studied both scenarios with …

Consider learning a decision support assistant to serve as an intermediary between (oracle)
expert behavior and (imperfect) human behavior: At each time, the algorithm observes an …

We present an extensive study of a key problem in online learning where the learner can opt
to abstain from making a prediction, at a certain cost. In the adversarial setting, we show how …