Mid-november | Choose a
project (one or two
students per projects, preferably two) |
Before 11/26 | Submit
proposal detailing the choice
of project.
More information on the procedure will provided soon. |
Before 12/03 | Send a draft (1 page) + first
results, on the Moodle. |
12/17 | Poster session in Batiment Cournot (C102-103) - 9am to 12pm |
Before 01/07 | Submit your project report (~6 pages, on the Moodle) |
Before 01/07 | Submit your take home exam (on the Moodle). |
Probabilistics PCA | Interpretation of PCA as a graphical model
close to factorial analysis. A situation where EM has no local
minima. 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
models |
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
filtering) |
A simulation for dynamic graphical models Chapter of "polycopie" 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 "polycopie" and articles. |
Dynamic graphical models | Chapter of "polycopie". Specific topics to be defined. |
General applications of the sum-product algorithms (e.g., to the FFT) | The
generalized
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. |
Dirichlet Process |
|
Factorial HMM |
Z. Ghahramani et M. I Jordan, Factorial
hidden
Markov models, Machine
learning 29, no. 2 (1997): 245-273 |
Generalized PCA |
M. Collins, S. Dasgupta, et R. E
Schapire, A
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.
Tibshirani, Sparse
inverse
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 (2007): 6242-6251. |
Bioinformatics | Chapter
of polycopie (ask login/pwd by e-mail) Phylogenetic HMM: 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 of "polycopie" on Kalman filtering) |
Text | Naive Bayes: 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. [ .ps.gz | .pdf | code ] |
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
Linguistics. 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
methods: 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) |