Preliminary program. Click the picture for a larger view.
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.