Machine Learning and Combinatorics Workshop

Research in theoretical computer science has been a source of deep connections between computer science and combinatorics. These connections are often symbiotic: on the one hand they provide formal frameworks to study algorithmic problems, and on the other hand they yield novel techniques that shed light on (and sometimes solve) long standing open problems in combinatorics.

In this workshop we focus on the combinatorial-algorithmic link in machine learning; a link which dates back to the discovery of the VC-dimension and PAC-learnability, and has various other manifestations in online learning, boosting, sample compression schemes, and more.

Omri Ben-Eliezer (Tel Aviv University, Israel)
Hunter Chase (University of Illinois at Chicago, US)
Steve Hanneke (Toyota Technological Institute at Chicago, US)
Andrey Kupavskii (MIPT, Moscow, Russia and CNRS, Grenoble, France)
Roi Livni (Tel Aviv University, Israel)
Shachar Lovett (University of California San Diego, US)
Shay Moran (Technion, Haifa, Israel)
Wolfgang Mulzer (Freie Universität Berlin, Gemrany)
János Pach (Alfred Renyi Institute, Budapest, Hungary and MIPT, Moscow, Russia)
Amir Yehudayoff (Technion, Haifa, Israel)