Welcome to the Machine Learning Reading Group at Brown University. We meet in CIT Room 506 on Fridays at 12pm and are open to all students and faculty.

MLRG meetings and other news are announced via posts to the ML-READING-GROUP list. You can subscribe here. The MLRG leader this semester is Aggeliki Tsoli.

If you have an ongoing or recently completed project involving machine learning, please offer to lead a session!

Date Topic Unit
January 23rd Bring papers and topics! Organizational
January 30th Classification of anti-learnable biological and synthetic data
Adam Kowalczyk (ECML/PKDD 2007)

More anti-learning related links:
February 6th Learning Gaussian Processes from Multiple Tasks
Kai Yu, Volker Tresp, Anton Schwaighofer (ICML 2005)
Multi-task Learning
February 13rd Learning Multiple Related Tasks using Latent Independent Component Analysis
Jian Zhang, Zoubin Ghahramani, Yiming Yang (NIPS 2005)
Multi-task Learning
February 27th Online Methods for Multi-Domain Learning and Adaptation
Mark Dredze, Koby Crammer (EMNLP 2008)
Multi-task Learning
March 6th Introduction to tensors
Joakim Strandberg
High-dimensional Learning
March 13th 3D City Modeling based on Hidden Markov Model
Florent Lafarge, Xavier Descombes, Josiane Zerubia, and Marc Pierrot-Deseilligny (ICIP 2007)
March 20th Tensor Subspace Analysis
Xiaofei He, Deng Cai, Partha Niyogi1
High-dimensional Learning
March 27th No meeting.
April 3rd The Information-Form Data Association Filter
Brad Schumitsch, Sebastian Thrun, Gary Bradski, Kunle Olukotun
High-dimensional Learning
April 10th Hierarchical Sampling for Active Learning
Sanjoy Dasgupta, Daniel Hsu
Active Learning
April 17th Cascaded Classification Models: Combining Models for Holistic Scene Understanding
Geremy Heitz, Stephen Gould, Ashutosh Saxena, Daphne Koller
April 24th Efficient Structure Learning of Markov Networks using L1-Regularization
Su-In Lee, Varun Ganapahthi, Daphne Koller
May 1st A General Agnostic Active Learning Algorithm
Sanjoy Dasgupta, Daniel Hsu, Claire Monteleoni
Active Learning
May 8th VC Dimension tutorial
Andrew Moore

A Tutorial on Support Vector Machines for Pattern Recognition (section 2)
Christopher Burges
May 22nd Structured Generative Models for Unsupervised Named-Entity Clustering (practice talk)
Micha Elsner

Conferences and journals:


Go forth and optimize!