NeurIPS 2019 Vancouver

The Neural Information Processing Systems Foundation is a non-profit corporation whose purpose is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. Neural information processing is a field which benefits from a combined view of biological, physical, mathematical, and computational sciences.

The primary focus of the Foundation is the presentation of a continuing series of professional meetings known as the Neural Information Processing Systems Conference or NeurIPS, held over the years at various locations in the United States, Canada and Spain.

The 2019 edition will take place in Vancouver, Canada. Florence d’Alché-Buc, the DSAIDIS chair holder, will be a NeurIPS 2019 co-chair.

Here is the list of the accepted papers :

Stochastic Conditional Gradient Method for Composite Convex Minimization
Francesco Locatello, Alp Yurtsever (LIONS, Ecole Polytechnique Fédérale de Lausanne, Switzerland), Olivier Fercoq (LTCI, Telécom Paris, Institut polytechnique de Paris, France) and Volkan Cevher (LIONS, Ecole Polytechnique Fédérale de Lausanne, Switzerland), NeurIPS 2019

RSN: Randomized Subspace Newton
Robert Gower (Télécom Paris, Institut polytechnique de Paris) · Dmitry Koralev (KAUST) · Felix Lieder (Heinrich-Heine-Universität Düsseldorf) · Peter Richtarik (KAUST)

Towards closing the gap between the theory and practice of SVRG
Othmane Sebbouh (Télécom Paris, Institut polytechnique de Paris) · Nidham Gazagnadou (Télécom Paris, Institut polytechnique de Paris) · Samy Jelassi (Princeton University) · Francis Bach (INRIA – Ecole Normale Superieure) · Robert Gower (Télécom Paris, Institut polytechnique de Paris)

Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Kimia Nadjahi (Télécom Paris, Institut polytechnique de Paris) · Alain Durmus (ENS Paris Saclay) · Umut Simsekli (Institut Polytechnique de Paris) · Roland Badeau (Télécom Paris, Institut polytechnique de Paris)

Generalized Sliced Wasserstein Distances
Soheil Kolouri (HRL Laboratories LLC) · Kimia Nadjahi (Télécom Paris, Institut polytechnique de Paris) · Umut Simsekli (Institut Polytechnique de Paris) · Roland Badeau (Télécom Paris, Institut polytechnique de Paris) · Gustavo Rohde (University of Virginia)

First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Thanh Huy Nguyen (Télécom Paris, Institut polytechnique de Paris) · Umut Simsekli (Institut Polytechnique de Paris) · Mert Gurbuzbalaban (Rutgers) · Gaël RICHARD (Télécom Paris, Institut polytechnique de Paris)