University of Southern California

Machine Learning Reading Group (MLRG) @ SAIL, USC

   

Fall 2010 Schedule (10 am Friday in RTH 320)

  • 3 Dec 2010

R. Albert and A. L. Barabasi, Statistical mechanics of complex networks, Reviews of Modern Physics, 2002.

Discussion Leaders: Dogan Can, Nassos Katsamanis, Kartik Audhkhasi

  • 24 Nov 2010

D. Mackay, Gaussian process basics, Gaussian Processes in Practice Workshop, 2006, Bletchley Park.

  • 19 Nov 2010

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.

  • 12 Nov 2010

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.

  • 5 Nov 2010

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

  • 29 Oct 2010

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.

  • 22 Oct 2010

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

  • 24 Sep 2010

B. Walsh, Markov chain monte carlo and Gibbs sampling, Lecture Notes for EEB 581.

Discussion Leaders: Vikram Ramanarayanan, Nassos Katsamanis