Some example papers.

This list is not exhaustive, these are just a few examples. Students can come up with other papers that they are interested in. The choice is entirely up to the students.


T. Toyoizumi, J.-P. Pfister, K. Aihara, W. Gerstner: Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model
Advances in Neural Information Processing Systems 17, MIT Press

G Chechik, A Globerson, N Tishby,Y Weiss Information Bottleneck for Gaussian variables.
J. Machine Learning Research 6(Jan) p.165-188, 2005

D. MacKay: Information-based objective functions for active data selection, Neural Computation 4 4 589-603

V. Balasubramanian, Statistical Inference, Occam's Razor and Statistical Mechanics on The Space of Probability Distributions, Neural Computation, Vol.9, No.2, Feb. 1997

Occam factors and model-independent Bayesian learning of continuous distributions. I Nemenman & W Bialek, Phys Rev E  65, 026137 (2002)


S. Tong, D. Koller: Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research. Volume 2, pages 45-66. 2001

A Bordes, S Ertekin, J Weston, L Bottou, Fast Kernel Classifiers with Online and Active Learning, Journal of Machine Learning Research, vol. 6, pp. 1579-1619, Sep. 2005


Field theories for learning probability distributions.  W Bialek, CG  Callan & SP Strong,  Phys Rev Lett  77, 4693-4697 (1996)


W Bialek, I Nemenman & N Tishby, Complexity through nonextensivity Physica A 302,
89-99 (2001)

R. Linsker, Self-organization in a perceptual network. Computer  21 (3) 105-17 (1988)

Chris Watkins and Peter Dayan. Q-Learning. Machine Learning, 8:279--292, 1992.




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