Abstract:

In this talk, we formalize a combinatorial optimization problem to speed up live experimentation. In digital channels across the web, mobile, and virtual agents, a large set of content is desirable to increase diversity. In contrast, a smaller set reduces the time it takes to collect training data for machine learning models. We show how to optimize for such conflicting criteria using multi-level optimization. Our approach integrates techniques from discrete optimization, unsupervised clustering, and latent text embeddings. More broadly, we present a hybrid framework that shows how to combine modern Recommender Systems and NLP with Optimization.

Bio:

Serdar Kadioglu is Group VP of AI at Fidelity Investments and Adjunct Associate Professor of Computer Science at Brown University. Previously, he led the Advanced Constraint Technology group at Oracle and worked at Adobe. Dr. Kadioglu’s research is at the intersection of AI and Optimization with practical interests in building robust and scalable products while contributing to the open-source ecosystem.