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Machine Learning Reading Group

Fall 2009

Welcome to the Machine Learning Reading Group at Brown University. We meet in CIT Room 345 on most Fridays at 12:00pm and are open to all students and faculty. (If you can't make this time, please email the group leader.)

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 Jason Pacheco.

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


Date Topic Discussion leaders
Sep 11 Organizational Meeting (no paper)  
Sep 18 Relational Topic Models for Document Networks
Jonathan Chang, David M. Blei
Soumya Ghosh
Sep 25 Spatially coherent latent topic model for concurrent object segmentation and classification
Liangliang Cao, Li Fei-Fei
Oct 2 (Section 4, pages 198 - 205)
An Introduction to Variational Methods for Graphical Models
M. I. Jordan, Z. Ghahramani, T. S. Jaakkola and L. K. Saul

Reference Only:
Graphical Models, Exponential Families, and Variational Inference
Martin J. Wainwright, Michael I. Jordan
Oct 9 (Section 2.1,2.2,2.3.1, pages 44-57)
Variational Algorithms for Approximate Bayesian Inference
Matthew J. Beal, PhD Thesis
Jason Pacheco
Oct 16 Censored Exploration and the Dark Pool Problem
Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, and Jennifer Wortman Vaughan
Oct 9 (Section 2.3.1, pages 53-63)
REVISIT: Variational Algorithms for Approximate Bayesian Inference
Matthew J. Beal, PhD Thesis
Oct 30 TALK: A Bayesian Sampling Approach to Exploration in Reinforcement Learning
J. Asmuth, L. Li, M. Littman, A. Nouri, D. Wingate
Paper assigned for talk!
New Location: Lubrano
Michael Littman
Nov 6 TALK: Censored Exploration and the Dark Pool Problem
Jennifer Wortman Vaughan
Nov 13 Introduction to Pitman-Yor Processes. Please choose the paper that best suits your level of familiarity with PY-processes:

NOVICE LEVEL: Modeling individual differences using Dirichlet processes
D. J. Navarro, T. L. Griffiths, M. Steyvers, and M. D. Lee

INTERMEDIATE LEVEL: Interpolating Between Types and Tokens by Estimating Power-Law Generators
S. Goldwater, T. L. Griffiths, M. Johnson

ADVANCED LEVEL: A Stochastic Memoizer for Sequence Data
F. Wood, C. Archambeau, J. Gasthaus, L. James, and Y.W. Teh.
Micha Elsner
Nov 23 TALK: A Stochastic Memoizer for Sequence Data (CIT 368, 4:00pm)
Frank Wood '07
Dec 4 Last MLRG of Fall 09
NIPS Pre-Cap: A preview of upcoming NIPS papers.