COMPUTATIONAL AND STATISTICAL TRADE-OFFS IN LEARNING
Organized by: Sylvain Arlot (Université Paris-Sud, Paris-Saclay), Francis Bach (INRIA Paris), Alain Celisse (Université de Lille 1)
This workshop focuses on the computational and statistical trade-offs arising in various domains (optimization, statistical/machine learning).
This is a challenging question since it amounts to optimize the performance under limited computational resources, which is crucial in the large-scale data context.
One main goal is to identify important ideas independently developed in some communities that could benefit the others.
Speakers :
Pierre Alquier (ENSAE, Paris-Saclay)
Alexandre d'Aspremont (D.I., CNRS/ENS Paris)
Quentin Berthet (DPMMS, Cambridge Univ., UK)
Alain Celisse (Université de Lille 1)
Rémi Gribonval (INRIA, Rennes)
Emilie Kaufmann (CNRS, Lille)
Vianney Perchet (CREST, ENSAE Paris-Saclay)
Garvesh Raskutti (Wisconsin Institute for Discovery, Madison, USA)
Ohad Shamir (Weizmann Institute, Rehovot, Israel)
Silvia Villa (Istituto Italiano di Tecnologia, Genova & MIT, Cambridge, USA)
