Reading material

Update: Exercise material and slides can be found under Invited Speakers, below the description of the relevant speaker.

Classics of semi-supervised learning:

  • Castelli, V. and Cover, T.M., 1995. On the exponential value of labeled samples. Pattern Recognition Letters, 16(1), pp.105-111.
  • Blum, A. and Mitchell, T., 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the eleventh annual conference on Computational learning theory (pp. 92-100). ACM.
  • Nigam, K., McCallum, A.K., Thrun, S. and Mitchell, T., 2000. Text classification from labeled and unlabeled documents using EM. Machine learning, 39(2-3), pp.103-134.
  • L.J.P. van der Maaten and K.Q. Weinberger. Stochastic Triplet Embedding. To appear in Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012.
  • Tamuz, Omer; Liu, Ce; Belongie, Serge; Shamir, Ohad; Kalai, Adam. Adaptively Learning the Crowd Kernel. International Conference on Machine Learning (ICML), Bellevue, WA, 2011.
  • Chopra, Sumit, Raia Hadsell, and Yann LeCun. “Learning a similarity metric discriminatively, with application to face verification.” CVPR 2005. 
  • Cohen, I., Cozman, F. G., Sebe, N., Cirelo, M. C., & Huang, T. S. (2004). Semisupervised learning of classifiers: Theory, algorithms, and their application to human-computer interaction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(12), 1553-1566.
  • Joachims, T. (1999, June). Transductive inference for text classification using support vector machines. In ICML (Vol. 99, pp. 200-209).
EM, self-learning, and assumptions in SSL
  • Loog, M. (2016). Contrastive pessimistic likelihood estimation for semi-supervised classification. IEEE transactions on pattern analysis and machine intelligence, 38(3), 462-475.
  • Krijthe, J. H., & Loog, M. (2016). Projected Estimators for Robust Semi-supervised Classification. arXiv preprint arXiv:1602.07865.
  • Loog, M., Krijthe, J. H., & Jensen, A. C., On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL, in: C. H. Chen (Ed.), Handbook of Pattern Recognition and Computer Vision, 5th Edition, World Scientific, 2016, Ch. 1.3.
Learning perceptual embeddings with triplet based comparisons:
  • Wilber, Michael; Kwak, Sam; Belongie, Serge. Cost-Effective HITs for Relative Similarity Comparisons, Human Computation and Crowdsourcing (HCOMP), Pittsburgh, 2014.
  • Wilber, Michael; Kwak, Iljung; Kriegman, David; Belongie, Serge. Learning Concept Embeddings with Combined Human-Machine Expertise, International Conference on Computer Vision (ICCV), 2015.
Variational auto-encoders and their application to semi-supervised learning:
  • Kingma, Diederik P and Mohamed, Shakir and Rezende, Danilo Jimenez and Welling, Max. Semi-supervised Learning with Deep Generative Models, Advances in Neural Information Processing Systems, p. 3581-3589, 2014.
  • Kingma, Diederik P and Welling, Max. Auto-Encoding Variational Bayes, arXiv preprint arXiv:1312.6114, 2013.
  • Rezende, Danilo Jimenez and Mohamed, Shakir and Wierstra, Daan. Stochastic Backpropagation and Approximate Inference in Deep Generative Models, arXiv preprint arXiv:1401.4082, 2014.


Bonus material:
  • Seeger, M., 2000. Learning with labeled and unlabeled data (No. EPFL-REPORT-161327)
  • Chapelle, O., Schölkopf, B. and Zien, A., 2006. Semi-Supervised Learning.