Preliminary program. Click the picture for a larger view.

Update: The bus leaves at 9.30 Monday, and we will be back Friday around 15.00.


Topics covered:

Lars Kai Hansen: Classics of semi-supervised learning
  • Exponential convergence in mixture models with known input distribution (Castelli-Cover result)
  • Co-training  (Blum, Mitchell)
  • Kernel methods (clustering hypothesis, label propagation)
  • Bayesian methods for principled integration of generative and discriminative models (Minka, Bishop Laserre)
Serge Belongie: Learning perceptual embeddings with triplet based comparisons
  • Learning Concept Embeddings
  • t-Stochastic Triplet Embedding (t-STE)
  • Crowd Kernel Learning (CKL)
  • Siamese Networks
Marco Loog: Minimax and projection estimators for semi-supervised learning
  • EM, self-learning, and assumptions in SSL
  • Safety, Contrast, and Pessimism
Ole Winther: Variational auto-encoders and their application to semi-supervised learning


For reading material go to: webpage.