The potential for machine learning (ML) systems to amplify social inequities and unfairness
is receiving increasing popular and academic attention. A surge of recent work has focused …

Logistic regression is by far the most widely used classifier in real-world applications. In this
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …

We study the problem of safe learning and exploration in sequential control problems. The
goal is to safely collect data samples from operating in an environment, in order to learn to …

In many learning settings, the source data available to train a regression model differs from
the target data it encounters when making predictions due to input distribution shift …

Studies of active learning traditionally assume the target and source data stem from a single
domain. However, in realistic applications, practitioners often require active learning with …

We pose the question to what extent a learning algorithm behaves monotonically in the
following sense: does it perform better, in expectation, when adding one instance to the …

Cutting-edge machine learning techniques often require millions of labeled data objects to
train a robust model. Because relying on humans to supply such a huge number of labels is …

The compensation system called komi has been used in scoring games such as Go. In Go,
White (the second player) is at a disadvantage because Black gets to move first, giving that …

Covariate shift relaxes the widely-employed independent and identically distributed (IID)
assumption by allowing different training and testing input distributions. Unfortunately …

Active learning methods, like uncertainty sampling, combined with probabilistic prediction
techniques have achieved success in various problems like image classification and text …