Machine Learning Reading Group
In this Section:
The machine learning reading group is an informal weekly meeting of those interested in advanced topics in machine learning. We read and discuss a series of recent papers from the ML and related literatures, chosen by group members as interest warrants. The purpose of the meeting is to discuss new ideas, improve all of our understanding, and, hopefully, to have some fun!
Current topics that interest us include, but are not limited to:
- Advanced methods for graphical models
- Variational and mean-field methods
- Structure search
- Advanced representations (e.g., relational learning)
- Relational, hybrid statistical/symbolic, and other higher-order representation and inference methods
- Reinforcement learning and stochastic planning
- Model factoring and decomposition
- Scaling
- Determinism and near-determinism
- MDP and POMDP approximation methods
- Interplay with representation/inference issues
- Kernel methods and support vector machines
- Non-stationarity, online learning, concept drift, and temporal models
- Topological methods
- Applications
- Bioinformatics, functional genomics, etc.
- Imaging, vision, handwriting recognition, etc.
- Security, intrusion detection, virus analysis, etc.
- Robotics, control, path plannning, etc.
Schedule and Attendance
The meetings are informal and attendance is completely voluntary -- if you're interested, you're welcome to attend sporadically as your schedule/interest allows. All we ask is that you attempt to read the selected paper for the week before coming to the meeting. Don't feel that you need to fully understand a paper --- a substantial goal is for us all to improve our understanding of these ideas --- but do please try to read the paper in whatever level of detail you're comfortable with.
Note: If you are likely to attend many or most of the reading group sessions for this semester, please sign up for the corresponding class section: "Sem: Adv Machine Lrng Topics - 25195 - CS 691B - 002". This is mostly for university and departmental accounting reasons -- there won't be any additional assignments/work associated with the seminar. If it will be an economic hardship for you to enroll in the class, please talk to Dr. Lane.
Mail List
If you would like to receive notifications of papers that we'll be reading, schedule changes, etc. then you should probably sign up for the MLRG mail list.
Meeting time and location
- Fall 2007: Wed, 12:00 - 1:30 PM, Farris Engineering Center, Terran's conference room (3rd floor by rooms 323-330b). Participants are invited to bring lunch.
This semester's Papers (Fall 2007)
- August 28
- Guange Dai and Dit-Yan Yeung "Tensor Embedding Methods"
- Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvarinen, Antti Kerminen "A Linear Non-Gaussian Acyclic Model for Causal Discovery"
- Chen Yanover, Talya Meltzer, Yair Weiss "Linear Programming Relaxations and Belief Propagation -- An Empirical Study"
- Guido Sanguinetti and Neil D. Lawrence "Missing Data in Kernel PCA"
- Alexander Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael Littman "PAC Model-free Reinforcement Learning"
- Rouhollah Rahmani, Sally Goldman "MISSL: Multiple-Instance Semi-Supervised Learning"
- Bowling, McCracken, James, Neufeld, Wilkinson "Learning Predictive State Representations using Non-Blind Policies"
- da Silva, Basso, Bazzan, Engel "Dealing with Non-Stationary Environments using Context Detection"
- Epshteyn, DeJong "Qualitative Reinforcement Learning"
- Bhat, Isbell, Mateas "On the Difficulty of Modular Reinforcement Learning for Real- World Partial Programming"
- Francois Denis francois, Christophe Nicolas Magnan, Liva Ralaivola "Efficient Learning of Naive Bayes Classifiers under Class-Conditional Classification Noise"
- Nima Asgharbeygi, David Stracuzzi, Pat Langley "Relational Temporal Difference Learning"
- Ricardo Silva, Richard Scheines "Bayesian Learning of Measurement and Structural Models"
- Guange Dai and Dit-Yan Yeung "Tensor Embedding Methods "
- Abbeel, Daphne Koller, Andrew Y. Ng "Learning Factor Graphs in Polynomial Time and Sample Complexity Pieter"
- STAIR. Leslie Kaelbling's STatistical AI Reading group at MIT.
Current candidate papers for this semester
Other related machine learning reading groups
Further information will be posted here as it becomes available.
