Many recommendation systems produce result sets with large numbers of highly similar
items. Diversifying these results is often accomplished with heuristics, which are …

Algorithmic recommendations shape music consumption at scale, and understanding the
impact of various algorithmic models on how content is consumed is a central question for …

Motivated by online decision-making in time-varying combinatorial environments, we study
the problem of transforming offline algorithms to their online counterparts. We focus on …

Educational platforms are increasingly becoming AI-driven. Besides providing a wide range
of course filtering options, personalized recommendations of learning material and teachers …

In this paper, we propose a novel framework that converts streaming algorithms for
monotone submodular maximization into streaming algorithms for non-monotone …

We investigate the performance of a deterministic GREEDY algorithm for the problem of
maximizing functions under a partition matroid constraint. We consider non-monotone …

Over the last two decades, submodular function maximization has been the workhorse of
many discrete optimization problems in machine learning applications. Traditionally, the …

In many machine learning applications, one needs to interactively select a sequence of
items (eg, recommending movies based on a user's feedback) or make sequential decisions …

We study the problem of active learning with the added twist that the learner is assisted by a
helpful teacher. We consider the following natural interaction protocol: At each round, the …

Motivated by applications in online advertising, we consider a class of maximization
problems where the objective is a function of the sequence of actions and the running …