The problem of how to genetically modify cells in order to maximize a certain cellular
phenotype has taken center stage in drug development over the last few years (with, for …

We explore the promises and challenges of employing sequential decision-making
algorithms--such as bandits, reinforcement learning, and active learning--in law and public …

In many predictive contexts (eg, credit lending), true outcomes are only observed for
samples that were positively classified in the past. These past observations, in turn, form …

In many real world settings binary classification decisions are made based on limited data in
near real-time, eg when assessing a loan application. We focus on a class of these …

We study a class of decision-making problems with one-sided feedback, where outcomes
are only observable for specific actions. A typical example is bank loans, where the …

Recent advances in machine learning (ML) have seen systems achieve human-like
performance in specific tasks, especially in the realms of sequential decision-making and …

I present a simple model of a credit market in which lenders use predictive models to
evaluate borrowers' credit risk. Each firm trades off its ability to predict borrowers' risk …

In many real world settings binary classification decisions are made based on limited data in
near real-time, eg when assessing a loan application. We focus on a class of these …