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.

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.

Google MathWorld

Advances in Neural Information Processing Systems 17, MIT Press

G Chechik, A Globerson, N Tishby,Y Weiss Information Bottleneck for Gaussian variables.

Occam factors and model-independent Bayesian learning of continuous distributions. I Nemenman & W Bialek,

S. Tong, D. Koller: Support Vector Machine Active Learning with Applications to Text Classification.

A Bordes, S Ertekin, J Weston, L Bottou, Fast Kernel Classifiers with Online and Active Learning,

Field theories for learning probability distributions. W Bialek, CG Callan & SP Strong,

W Bialek, I Nemenman & N Tishby, Complexity through nonextensivity

R. Linsker, Self-organization in a perceptual network.

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

Google MathWorld