Machine Learning Reading Group (MLRG) @ SAIL, USC
Fall 2010 Schedule (10 am Friday in RTH 320)
R. Albert and A. L. Barabasi, Statistical mechanics of complex networks, Reviews of Modern Physics, 2002.
Discussion Leaders: Dogan Can, Nassos Katsamanis, Kartik Audhkhasi
D. Mackay, Gaussian process basics, Gaussian Processes in Practice Workshop, 2006, Bletchley Park.
C. W. J. Granger, Some recent development in a concept of causality, Journal of Econometrics, vol. 39, issue 1-2, pp. 199-211, September-October 1988.
K. Prabhakar et. al, Temporal causality for the analysis of visual events, Proc. CVPR, 2010.
M. Ding, Y. Chen and S. L. Bressier, Granger causality: Basic theory and application to neuroscience, pp. 451-474, Handbook of Time Series Analysis, editors B. Schelter, M. Winterhalder and J. Timmer, Wiley-VCH Verlage, 2006.
A. Seth, Granger causality, Scholarpedia, 2(7):1667, revision 73390.
Discussion Leaders: Angeliki Metallinou, Nassos Katsamanis.
Part-2 of Lecture by R. E. Schapire, A boosting tutorial, Machine Learning Summer School, Chicago, 22005.
R. Meir and G. Ratsch, A introduction to boosting and leveraging, Advanced Lectures in Machine Learning, LNAI, pp. 118-183, Springer-Verlag Berlin
R. E. Schapire, A boosting approach to machine learning, MSRI Workshop on Nonlinear Estimation and Classification, 2002.
H. Narayanan and P. Niyogi, Language evolution, coalescent processes, and the consensus problem on a social network, Submitted to Proc. of National Academy of Sciences.
Discussion Leaders: Mike Proctor, Kartik Audhkhasi
Part-1 of Lecture by R. E. Schapire, A boosting tutorial, Machine Learning Summer School, Chicago, 2005.
Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, Proc. ICML, pp. 148-156, 1996.
Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997.
M. A. T. Figueiredo and A. K. Jain, Unsupervised learning of finite mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, March 2002.
Discussion Leaders: Ming Li, Nassos Katsamanis, Kartik Audhkhasi
B. Walsh, Markov chain monte carlo and Gibbs sampling, Lecture Notes for EEB 581.
Discussion Leaders: Vikram Ramanarayanan, Nassos Katsamanis
|