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