Carnegie Mellon Machine Learning Lunch seminar

	Dec 10, 2007 Maximum Likelihood Estimation in Latent Class Models for Contingency Table Data Yi Zhou
	Dec 3, 2007 Causal discovery based on non-gaussianity Patrick Hoyer
	Nov 26, 2007 Statistical Parsing Triptych: Jeopardy, Morphosyntax, and M-Estimation Noah Smith
	Nov 19, 2007 The Maximum Entropy Principle Miroslav Dudik
	Nov 12, 2007 Spatiotemporal Stochastic Processes and Their Prediction Cosma Shalizi
	Nov 05, 2007 Stochastic Processes and their Prediction Cosma Shalizi
	Oct 29, 2007 Proximity on Graphs: Definitions, Fast Solutions and Applications Hanghang Tong
	Oct 22, 2007 Machine Learning in in vivo CNS Drug Discovery Jeff Schneider
	Oct 16, 2007 (Tue) Visualizing Social Media: Principles and Techniques Matthew Hurst
	Oct 1, 2007 Random Walks on Graphs: A General Overview Purnamrita Sarkar
	Sept 24, 2007 Some Topics in Spam Filtering D. Sculley
	May 7, 2007 Probabilistic Inference in Distributed Systems Stanislav Funiak
	Apr 23, 2007 Learning without the loss function John Langford
	Apr 16, 2007 Sparsity recovery and structure learning Pradeep Ravikumar
	April 2, 2007 A unifying view of component analysis (from a computer vision perspective) Fernando De la Torre
	Mar 19, 2007 Active Learning of Binary Classifiers Nina Balcan
	Mar 5, 2007 Features, kernels, and similarity functions Avrim Blum
	Feb 26, 2007 Models of real-world networks (Part II) Jure Leskovec
	Feb 19, 2007 The structure and function of real-world graphs and networks (Part I) Jure Leskovec
	Feb 12, 2007 Discrete Markov Random Fields -- the Inference story Pradeep Ravikumar
	Jan 22, 2007 NIPS 2006 Conference Review Session.
	Jan 22, 2007 Greedy Layer-Wise Training of Deep Networks. Nathan Ratliff
	Jan 22, 2007 Approximate inference using planar graph decomposition. Pradeep Ravikumar