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!
Schedule
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 |
Everyone |
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 |
Everyone |
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 |
Everyone |
Oct 9 | (Section 2.3.1, pages 53-63) REVISIT: Variational Algorithms for Approximate Bayesian Inference Matthew J. Beal, PhD Thesis |
Volunteer! |
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. |
Everyone |
Resources
- MLRG 2009: Summer Spring
- MLRG 2008: Fall Summer Spring
- MLRG 2007: Fall Summer Spring
- Computational Modeling Reading Group
- Pattern Theory Lunch Seminar