ICML 2020
Skip to yearly menu bar Skip to main content


Negative Dependence and Submodularity: Theory and Applications in Machine Learning

Zelda Mariet · Michal Derezinski · Mike Gartrell

Keywords:  Recommender Systems    submodularity    negative dependence    subset selection    experimental design    determinantal point processes  

Models of negative dependence and submodularity are increasingly important in machine learning. Whether selecting training data, finding an optimal experimental design, exploring in reinforcement learning and Bayesian optimization, or designing recommender systems, selecting high-quality yet diverse items has become a core challenge. This workshop aims to bring together researchers who, using theoretical or applied techniques, leverage negative dependence and submodularity in their work. Expanding upon last year's workshop, we will highlight recent developments in the rich mathematical theory of negative dependence, cover novel critical applications, and discuss the most promising directions for future research.

Chat is not available.
Timezone: America/Los_Angeles