The project gives you the opportunity to study in greater depth some concepts of the course. The topic has to be linked with algorithms, concepts or methods presented in class, but beyond this requirement, the choice is quite open. In particular, it may be tailored to your interests. We encourage you to choose a paper that closely fits your interests, and any personal original contribution is valued.
The standard class projects need to contain the following 3 components:
An article review around a given topic (research articles or chapter from Mike's book not studied in class). See below for a list of tentative projects. This means to read and understand a specific research article.
An implementation of the method.
An experimentation with real data. This means to apply the method on real data and report your findings and observations. If the paper is quite dense and theoretical, then an experimentation on simulated / synthetic data is sufficient.
The project may be done alone or in groups of two. Once you have an idea of a project, it is mandatory to have it validated by the teacher by submitting a quick description of it on Studium.
The final class project counts for 30%. Evaluation will be made on:
A report (of about 4 to 8 pages) presenting the project and the obtained results (for applicative projects), to be given by December 20th, 2016 on Studium. The report has to be written in such a way that any student who has followed the class can understand (no need to introduce graphical model concepts). The report has to clearly present (in French or English) the studied problem and the existing approaches. You will be more evaluated on the clarity of the report rather than on its length. To train you to write professional research papers, you should use LaTeX in the ICML 2016 template format (download the template here). You may use appendices for additional details beyond 8 pages if you want, but be aware that as in standard conference reviewing, I might only read the first 8 pages (so the main content has to be there), and also, succinctness is more valued here than length!
A poster presentation of 6 minutes, from at most 6-8 letter-sized pages (or as a poster format if you fancy it, but this is not required) which will be displayed on rolling boards during a poster session on Tuesday December 13th from 1:00pm to 3:30pm in room 6225 of André-Aisenstadt (Maurice-Labbé lounge). Note the earlier starting time than usual (the room was taken later so we need to start earlier). The presentation (in French or English) is also geared towards other students and the goal is to highlight in the allocated time the salient points of your project. Like in a regular conference poster session, students are encouraged to attend other student posters. Other guidelines for the poster and presentation:
The poster should be in English so that all students can understand it (but your presentation to me can be in French if you prefer).
The content of your poster has a double purpose:
to explain clearly to other students of the class the model, problems and algorithms you have worked on, and any interesting observations that you have made.
to be the support of your 6 minutes oral presentation of your project
The 6 minutes timing will be strict as we want to be able to ask you a couple of questions in addition and there are many of you. We highly recommend that you prepare ahead of time what you will say during these 6 minutes. Highlight your understanding and the main things you have done (model, main algorithmic ideas, data, results).
Each student or group of students has to obtain the agreement of the teacher on their project by submitting a short description of it on Studium by November 11th.
Each student or group of students has to submit a small mid-project report (one page pdf document) presenting the progress and the obtained results, so that some feedback may be given. This report has to submitted on Studium before November 29th.
The various steps are summarized below.
|End of October||Choose a project (one or two students per projects, preferably two).|
|Before 11/11||Choose your group and give your project choice on Studium.|
|Before 11/29||Send a draft (1 page) + first results on Studium.|
|On 12/13||Poster session in AA6225 - 1pm to 3:30pm|
|Before 12/20||Submit your project report (4-8 pages, ICML format, on Studium)|
The goal of this list of projects is to show classical (and if possible interesting) articles using or improving graphical models. This may give you an idea of current research topics as well as applicative projects. Even if you don't select any of these, reading some of them is advised.
|Probabilistics PCA||Interpretation of PCA as a graphical model
close to factorial analysis. A situation where EM has no local
Tipping, M. E., Bishop, C. M. 1999. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61(3):611-622. [pdf]
|Learning graph structure - multinomial
||For complete discrete data, learning of
parameters and directed acyclic graph.
D. Heckerman, D. Geiger, D. Chickering. Learning Bayesian networks: The Combination of Knowledge and Statistical Data. Machine Learning, 20:197-243, 1995.
|Learning graph structure - Gaussian models
||For complete Gaussian data, learning of
parameters and directed acyclic graph.
D. Geiger, D. Heckerman. Learning Gaussian networks. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pp. 235--243.
|Variational methods for inference||Class of method for approximate inference.
An introduction to variational methods for graphical models. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. In M. I. Jordan (Ed.), Learning in Graphical Models, Cambridge: MIT Press, 1999
Its application to Bayesian inference.
Beal, M.J. and Ghahramani, Z.
Variational Bayesian Learning of Directed Graphical Models with Hidden Variables
To appear in Bayesian Analysis 1(4), 2006.
|Simulation methods for inference (particle
||A simulation for dynamic graphical models
Chapter from Kevin Murphy
S. Arulampalam, S. Maskell, N. J. Gordon, and T. Clapp, A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking, IEEE Transactions of Signal Processing, Vol. 50(2), pages 174-188, February 2002.
Doucet A., Godsill S.J. and Andrieu C., "On sequential Monte Carlo sampling methods for Bayesian filtering," Statist. Comput., 10, 197-208, 2000
|Semi-Markovian models||A class of models allowing to model the time
spent in any given state for a Markov Chain and an HMM.
Note from Kevin Murphy [pdf]
|Learning parameters in an undirected graphical model (Markov random fields)||Chapter 9 of Mike's book and articles.|
|Dynamic graphical models||Chapter from Kevin Murphy. Specific topics to be defined.|
|General applications of the sum-product algorithms (e.g., to the FFT)||The
distributive law, S. M. Aji, R. J. Mceliece
Information Theory, IEEE Transactions on, Vol. 46, No. 2. (2000), pp. 325-343.
|Independent Component Analysis||A. Hyvarinen, E. Oja (2000): Independent
Component Analysis: Algorithms and Application, Neural
Networks, 13(4-5):411-430, 2000.
Course of Herve LeBorgne: http://www.eeng.dcu.ie/~hlborgne/pub/th_chap3.pdf
Canonical Correlation Analysis
|CCA is analogous to PCA for the joint analysis of two random
vectors X and Y.
Clustering through a mixture of PCA
|M. E Tipping et C. M Bishop, Mixtures of probabilistic principal component analyzers, Neural computation 11, no. 2 (1999): 443-482.|
Stochastic relational models
Conditional Random Fields
|Charles Sutton, Andrew McCallum An Introduction to Conditional Random Fields for Relational Learning . In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning. MIT Press. 2007.|
|Z. Ghahramani et M. I Jordan, Factorial
Markov models, Machine
learning 29, no. 2 (1997): 245-273
| M. Collins, S. Dasgupta, et R. E
generalization of principal component analysis to the exponential
family, Advances in neural
information processing systems 1 (2002): 617-624.
Structure learning by L1 regularization
|J. Friedman, T. Hastie, et R.
covariance estimation with the graphical lasso, Biostatistics
9, no. 3 (2008): 432.
Mixture of log-concave densities
| Interesting non-parametric
models for unimodal distributions.
distributions, Computational Statistics & Data Analysis 51, no. 12
These articles present classical applications. They may give you ideas for an applicative project or may be used for article reviews.
|Bioinformatics||Chapter 23 of Mike's book.
A. Siepel et D. Haussler, Phylogenetic hidden Markov models, Statistical methods in molecular evolution (2005), 3, 325-351.
|Vision/Speech||Articles from Kevin Murphy:
"Using the Forest to See the Trees:A Graphical Model Relating Features, Objects and Scenes" Kevin Murphy, Antonio Torralba, William Freeman. NIPS'03 (Neural Info. Processing Systems)
Dynamic Bayesian Networks for Audio-Visual Speech Recognition A. Nefian, L. Liang, X. Pi, X. Liu and K. Murphy. EURASIP, Journal of Applied Signal Processing, 11:1-15, 2002
Optimization for MAP inference in computer vision:
MRF Optimization via Dual Decomposition: Message-Passing Revisited, Komodakis, Paragios, Tziritas, ICVV 2007. Longer technical report version
|Robotics||Automatic construction of maps
Simultaneous Localization and Mapping with Sparse Extended Information Filters
Thrun et al. The International Journal of Robotics Research.2004;
(see also chapter 15 of Mike's book on Kalman filtering)
A. McCallum and K. Nigam. A comparison of event models for Naive Bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization, 1998.
Latent Dirichlet allocation. D. Blei, A. Ng, and M. Jordan. Journal of Machine Learning Research, 3:993-1022, January 2003. [.pdf | code]
topic modeling webpage
|Text - Natural language processing||S. Vogel, H. Ney, and C. Tillmann.
HMM-based word alignment in statistical translation. In Proceedings
of the 16th conference on Computational linguistics, pp.
836-841, Morristown, NJ, USA, 1996. Association for Computational
Non contextual probabilistic grammars:
Notes de cours de CMU, 1999
|N most probable configurations||Implementation of an algorithm (HMM or more
complex graphs), from the following articles:
Dennis Nilsson, Jacob Goldberger. An Efficient Algorithm for Sequentially finding the N-Best List , IJCAI, 1999
Chen Yanover, Yair Weiss, Finding the M Most Probable Configurations Using Loopy Belief Propagation, NIPS 2003.
|Computation of tree-width||Comparing the classical heuristics and finer
Mark Hopkins and Adnan Darwiche
A Practical Relaxation of Constant-Factor Treewidth Approximation Algorithms
Proceedings of the First European Workshop on Probabilistic Graphical Models 2002
Also some exact methods
Stefan Arnborg, Derek G. Corneil, Andrzej Proskurowski, Complexity of finding embeddings in a k-tree, SIAM Journal on Algebraic and Discrete Methods (1997)
Last modified: 2016-12-12 23h30