CSTL 2016 - Workshop on Computational and statistical trade-offs in learning

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.
Invited 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)
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 Insitute, Rehovot, Israel)
Silvia Villa (Istituto Italiano di Tecnologia, Genova & MIT, Cambridge, USA)