Each day is organized around two sessions, resp. from 9:00am to 12:00pm and from 5:10pm to 6:30pm. Lunch is at 12:30am sharp. Then the afternoon (from 2pm to 5pm) is free for scientific discussions, possibly on skis.

Note that there is no morning session on Wednesday. Talks resume at 5:10pm.


Monday, January, 12:
Session 1

  • 08:50–09:00 Welcome
  • 09:00–9:40 Shai Shalev-Shwartz (The Hebrew University)
    Stochastic Optimization for Deep Learning, [Slides]
  • 9:40–10:20 Tong Zhang (Rutgers University)
    Modern Optimization Techniques for Big Data Machine Learning.
  • 10:20–10:40   Break
  • 10:40–11:20 Jean-Philippe Vert (Mines ParisTech)
    New matrix norms for sparse and low-rank matrix estimation, [Slides]
  • 11:20–12:00 Arnak Dalalyan (CREST-ENSAE)
    Guarantees for Sampling from a log-concave density by Langevin Monte Carlo, [Paper]

Session 2

  • 17:10–17:50 Yuri Nesterov (Universite Catholique de Louvain)
    New primal-dual subgradient methods for convex optimization problems with functional constraints, [Slides]
  • 17:50–18:30 Sebastien Bubeck (Miscrosoft Research)
    The entropic barrier: a simple and optimal universal self-concordant barrier, [Paper]

Tuesday, January, 13:
Session 1

  • 09:00–9:40 Jean Bernard Lasserre (CNRS, LAAS)
    Reconstruction of algebraic-exponential data from moments, [Slides]
  • 9:40–10:20 Venkat Chandrasekaran (Caltech)
    Relative Entropy Relaxations for Signomial Optimization.
  • 10:20–10:40   Break
  • 10:40–11:20 Ken Clarkson (IBM Almaden)
    Sketching and Sampling for M-estimators, [Slides]
  • 11:20–12:00 Aleksander Madry (MIT)
    Interior-point Methods and the Maximum Flow Problem, [Slides]

Session 2

  • 17:10–17:50 John Lafferty (University of Chicago)
    High dimensional convex function estimation.
  • 17:50–18:30 Marco Cuturi (Kyoto University)
    The Wasserstein Barycenter Problem, [Slides]

Wednesday, January, 14:

  • 17:10–17:50 Alekh Agarwal (Microsoft Research)
    Learning sparsely used overcomplete dictionaries, [Slides]
  • 17:50–18:30 Peter Richtarik (University of Edinburgh)
    Coordinate descent methods with arbitrary sampling, [Slides]

Thursday, January, 15:
Session 1

  • 09:00–9:40 Elad Hazan (Princeton University)
    Overcoming NP-hardness by agnostic non-proper learning, [Slides]
  • 9:40–10:20 Afonso Bandeira (Princeton University)
    Tightness of SDP relaxations for certain inverse problems on graphs.
  • 10:20–10:40   Break
  • 10:40–11:20 Sasha Rakhlin (University of Pennsylvania)
    Randomized methods for 0th order optimization.
  • 11:20–12:00 Lorenzo Rosasco (MIT, IIT)
    Iterative Convex Regularization, [Slides]

Session 2

  • 17:10–17:50 Robert Krauthgamer (Weizmann Institute)
    Spectral Approaches to Nearest Neighbor Search, [Slides]
  • 17:50–18:30 Vladimir Spokoiny (Weierstrass Institute)
    Bootstrap confidence sets under model misspecification.

Friday, January, 16:

  • 09:00–9:40 Constantine Caramanis (TU Austin)
    On Detecting Epidemics from Weak Signatures.
  • 9:40–10:20 Fajwel Fogel (Ecole Normale Superieure)
    Seriation and ranking problems: spectral approach, [Slides]
  • 10:20–10:40   Break
  • 10:40–11:20 Karthik Sridharan (Cornell University)
    Relaxations: Deriving algorithms for learning and optimization.
  • 11:20–12:00 John Duchi (Stanford)
    Randomized smoothing techniques in optimization.