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