
Dec 10, 2007
Maximum Likelihood Estimation in Latent Class Models for Contingency Table Data
Yi Zhou


Dec 3, 2007
Causal discovery based on nongaussianity
Patrick Hoyer


Nov 26, 2007
Statistical Parsing Triptych: Jeopardy, Morphosyntax, and MEstimation
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 realworld networks (Part II)
Jure Leskovec


Feb 19, 2007
The structure and function of realworld 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 LayerWise Training of Deep Networks.
Nathan Ratliff


Jan 22, 2007
Approximate inference using planar graph decomposition.
Pradeep Ravikumar
