LearningTheory@FoCM'11

Speakers

• Ery Arias-Castro, UC San Diego, USA

• Francis Bach, INRIA Paris, France
  Non-asymptotic analysis of stochastic approximation algorithms for machine learning

• Nina Balcan, Georgia Tech, USA
  Learning valuation functions

• Peter Bartlett, UC Berkeley, USA
  Optimal regret in online prediction

• Elad Hazan, The Technion, Israel
  Information-optimal optimization in machine learning

• Mohamed Hebiri, Université Paris-Est Marne-la-Vallée, France
  The Smooth-Lasso estimator

• Samory Kpotufe, MPI Tubingen, Germany
  Pruning nearest neighbor cluster trees

• Shie Mannor, The Technion, Israel
  All learning is robust

• Sergei Pereverzyev, Austrian Academy of Sciences, Austria
  A Meta-learning approach to the regularized learning

• Luis Rademacher, Ohio State University, USA
  Randomized algorithms for the approximation of matrices

• Philippe Rigollet, Princeton University, USA
  Fast rates for nonparametric bandits with covariates using adaptive partitioning

• Shai Shalev-Shwartz, The Hebrew University, Israel
  Learnability beyond uniform convergence

• Joel Tropp, Caltech, USA

• Nicolas Vayatis, ENS Cachan, France

Schedule

	July 12	July 13	July 14
2:00-2:45	Nicolas Vayatis	Shie Mannor (semi-plenary)	Philippe Rigollet
2:50-3:35	Ery Arias-Castro	Nina Balcan	Samory Kpotufe
3:40-4:25	Francis Bach	Shai Shalev-Shwartz	Peter Bartlett (semi-plenary)
Coffee
5:00-5:45	Luis Rademacher	Elad Hazan	Sergei Pereverzyev
5:50-6:35	Mohamed Hebiri	Joel Tropp

Related plenary talks: Dave Donoho and Steve Smale

Related workshops of FoCM'11: Approximation theory

Previous workshops: LearningTheory@FoCM'08

Organizers