CSSS 2008 BeijingReadingsWeekThree
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CSSS 2008 Beijing

Contents
Lee Altenberg
Lecture Notes
Additional Reading
 Altenberg, L. , 2004. Open Problems in the Spectral Analysis of Evolutionary Dynamics presents a mathematical framework for evolutionary optimization and some of its unsolved problems.
 Altenberg, L. , 1994. The Schema Theorem and Price's Theorem delves into the claims about schema processing as the source of power in genetic algorithms, and recasts the Schema Theorem (Holland 1975) by using Price's Theorem (1970). It is shown that the Schema Theorem says nothing about a GA's power, but a modification with a different measurement function produces a theorem about evolvability that is a local measure of GA power. The concept of rugged landscapes is also deconstructed in terms of operatordefined distance.
 Erik van Nimwegen, James P. Crutchfield, and Martijn Huynen. Neutral Evolution of Mutational Robustness. Proceedings of the National Academy of Science U.S.A. 96:97169720 (1999).
 Altenberg, L. , 2004. Modularity in Evolution: Some Low Level Questions deconstructs the concept of modularity in terms of spaces of variation, and discusses properties needed for modularity to enhance evolvability.
 Erik van Nimwegen and James P. Crutchfield. Metastable Evolutionary Dynamics: Crossing Fitness Barriers or Escaping via Neutral Paths? Bulletin of Mathematical Biology 62:5:799848 (Sep 2000)
 Lauren Ancel Meyers, Fredric D. Ancel, Michael Lachmann. 2005. Evolution of Genetic Potential. PLoS Computational Biology 1(3): e32.
 Evolutionary Art at Electric Sheep
David Feldman
 Thoughts on Research Topics, Questions, and Problems
 Tips and Advice for giving Effective Research Presentations
(Samuel) QingShan Jia
Lecture Notes
 Ordinal Optimization: Soft Optimization for Hard Problems
 How does Spare Capacity and Topology Affect the Security of Resource Networks?
Additional Reading
 An Explanation of Ordinal Optimization. Ho. Information Sciences 113, 169192 (1999).
 Universal Alignment Probabilities and Subset Selection for Ordinal Optimization. Lau and Ho. Journal of Optimization Theory and Applications 93:3. 455489 (1997).
 How Topology Affects Security: An Upper Bound of Electric Power Network Security. Jia and Zhao.
 How Much Spare Capacity is Necessary for the Security of Resource Networks? Zhao, Jian and Cao. Physica A 373, 861873 (2007).
Susanne Still
Lecture Notes
July 14. A brief introduction to uses of information theory in machine learning
July 15. Optimal Causal Inference
Additional reading
You can find links to the following papers (and more) here.
 C. E. Shannon: A mathematical theory of communication, Bell System Technical Journal, vol. 27, pp. 379423 and 623656, July and October, 1948.
 K. Rose, "Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems," Proceedings of the IEEE, vol. 80, pp. 22102239, November 1998.
 N. Tishby, F. C. Pereira, & W. Bialek, “The information bottleneck method”, in Proceedings of the 37th Annual Allerton Conference on Communication, Control and Computing, B Hajek & RS Sreenivas, eds, pp 368377 (University of Illinois, 1999)
 Books:
 D. MacKay, "Information Theory, Inference and Learning Algorithms"
 T. M. Cover and J. A. Thomas, "Elements of Information Theory", Wiley.
Please visit my website for my papers related to the material covered in the lectures:
 S. Still and W. Bialek. “How many clusters? An information theoretic perspective”. 2004
 S. Still, W. Bialek and L. Bottou. “Geometric Clustering using the Information Bottleneck method”. 2004.
 S. Still and J. P. Crutchfield. Structure or Noise? 2007.
 S. Still, J. P. Crutchfield and C. J. Ellison. Optimal Causal Inference. 2007.
 S. Still. Statistical Mechanics approach to interactive learning. 2007.
Chris Wiggins
Lecture Notes
 Bayes
 History
 What he said
 What he didn't say
 Bayes rule in Dynamics
 Random walks
 Pollen grains
 Wall street
 Chemical kinetics
 Polymer Physics
 Random walks
 Bayes rule in Statistics
 Why we fit
 Regularization
 Latent variables / Mixture modeling
 Model selection the Bayesian way
 Bayesian density estimation / unsupervised learning
 Alternatives to Bayes
 Cross validation
 pvalueology
 Information theoretic methods
Slides for Chris's third lecture: Inferring and Encoding Graph Partitions . Also available here.
Additional Reading
 wikipedia (be careful! there's likely to be at least one error per page):
 http://en.wikipedia.org/wiki/Thomas_Bayes
 http://en.wikipedia.org/wiki/Bayes'_theorem
 http://en.wikipedia.org/wiki/Gamma_function
 http://en.wikipedia.org/wiki/Stirling%27s_approximation
 http://en.wikipedia.org/wiki/Method_of_steepest_descent#The_idea_of_Laplace.27s_method
 http://en.wikipedia.org/wiki/Gaussian_integral
 bayes's biography: http://wwwgap.dcs.stand.ac.uk/~history/Mathematicians/Bayes.html
 papers:
 bayes's original essay: http://www.stat.ucla.edu/history/essay.pdf
 mackay's thesis, Chapter 2. (take a look at his book, too below) http://www.inference.phy.cam.ac.uk/mackay/PhD.html
 nice Occam’s razor paper by Zoubin Ghahramani http://learning.eng.cam.ac.uk/zoubin/papers/05occam/occam.pdf
 schwartz's paper http://www.math.tau.ac.il/~yekutiel/MA%20seminar/Schwarz%201978.pdf
 those of you trained in statistical field theory will like this paper also http://arxiv.org/abs/adaporg/9601001
 mackay's book: http://www.inference.phy.cam.ac.uk/mackay/itila/book.html
 references to books not available online: