ORGANISERS:

• Steve SMALE (Toyota Institute, Chicago University)
• David MCALLESTER (Toyota Institute, Chicago University)
• Tomaso POGGIO (MIT)
• Gabor LUGOSI (Universitat Pompeau Fabra, Barcelona)

SPEAKERS (so far):

• Avrim BLUM (Carnegie-Mellon)
  "Kernels as features: On kernels, margins, and low-dimensional mappings" (abstract)
• Andrea CAPONNETTO (MIT)
  "Model selection and fast rates for regularized least-squares" (abstract)
• Nicolo CESA-BIANCHI (U Milano)
  "Regularized least squares for classification" (abstract)
• Ron DEVORE (U South Carolina)
  "Adaptivity in learning" (abstract)
• Sara van de GEER (RU Leiden)
  "A theoretical comparison of regularized classifiers"(abstract)
• Mike KEARNS (U Pennsylvania)
  "Learning from data of variable quality" (abstract)
• Sayan MUKHERJEE (Duke University)
  "Permutation tests for classification" (abstract)
• Partha NIYOGI (Chicago)
  "A geometric perspective on learning theory and algorithms"(abstract)
• Tomaso POGGIO (MIT)
  "Learning: new theoretical results and old applications"(abstract)
• Cynthia RUDIN (NYU)
  "AdaBoost: The expected but untrue, and the unexpected but true" (abstract)
• Steve SMALE (Chicago):
  "Learning theory algorithms and estimates without covering numbers" (abstract)
• Alessandro VERRI (U Genoa)
  "Functional methods for learning theory"(abstract)
• Vladimir VOVK (RHBC, London)
  "Defensive forecasting for decision making"(abstract)
• Martin WAINWRIGHT (UC Berkeley)
  "Stable message-passing and convex surrogates for learning in Markov random fields" (abstract)
• Ding ZHOU (CU Hong Kong)
  "Learning variable covariances via gradients" (abstract)

TIMETABLE:

Day 1: 4 July

Day 2: 5 July

Day 3: 6 July

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