Probabilistic Inference Group (PIGS) - Archive

For current meetings go to the main PIGS page.

Meetings from 2002–2006

Meetings were generally held on Tuesdays at 2 p.m. in C23, Forrest Hill.

Suggestions for papers were managed by Lawrence Murray.

2006-10-24 Transfer learning

2006-10-17 PIGlets: MCMC continued

2006-10-10 Kian Ming

Rie Kubota Ando and Tong Zhang. A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data. JMLR 2005.

Bart Bakker and Tom Heskes. Task Clustering and Gating for Bayesian Multitask Learning. JMLR 2005.

2006-10-02 PIGlets: MCMC continued

2006-09-26 UAI 2006 feedback session

Kian Ming: F. Ferns, P. S. Castro, D. Precup and P. Panangaden. Methods for computing state similarity in Markov Decision Processes.

Moray: Monika Schaeffer and Ronald Parr. Efficient selection of disambiguating actions for stereo vision.

Edwin: Zhao Xu, Volker Tresp, Kai Yu and Hans-Peter Kriegel. Infinite Hidden Relational Models.

Wolfgang: Matthew Beal and Praveen Krishnamurthy. Gene expression time course clustering with countably infinite Hidden Markov Models

2006-09-19 PIGlets: MCMC

Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan. An Introduction to MCMC for Machine Learning. Machine learning, 50, 5-43, 2003.

2006-09-12 UAI 2006 feedback session

Tom: T. Silander and P. Myllymäki. A simple approach for finding the globally optimal Bayesian network structure.

John: M. Eichler. Fitting graphical interaction models to multivariate time series.

Felix: M. Welling and S. Parise. Bayesian random fields: the Bethe-Laplace approximation.

Felix: M. Warmuth and D. Kuzmin. A Bayesian probability calculus for density matrices.

Felix: A. Choi and A. Darwiche. A variational approach for approximating Bayesian networks by edge deletion.

Felix: G. Mei and C. Shelton. Visualization of collaborative data.

Felix: W. Wiegerinck, B. van den Broek and B. Kappen. Stochastic optimal control in continuous space-time multi-agent systems.

Felix: F. Wood, T. L. Griffiths and Z. Ghahramani. A non-parametric Bayesian method for inferring hidden causes.

Chris: Z. Zhang and M. I. Jordan. Bayesian Multicategory Support Vector Machines

Moray: G. Lebanon. Sequential document representations and simplicial curves.

Amos: Inmar Givoni, Vincent Cheung and Brendan J. Frey. Matrix Tile Analysis.

Wolfgang: Iain Murray, Zoubin Ghahramani and David MacKay. MCMC for doubly-intractable distributions.

2006-08-21 John Quinn

Vikash Mansinghka, Charles Kemp, Thomas Griffiths and Joshua Tenenbaum. Structured Priors for Structure Learning. UAI 2006.


G. E. Hinton and R. R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science 28 July 2006: Vol. 313. no. 5786, pp. 504--507.

Gary Cottrell. New Life for Neural Networks. Science 28 July 2006: Vol. 313. no. 5786, pp. 454--455.

2006-07-24 Felix Agakov

Arik Azran, Zoubin Ghahramani. "A New Approach to Data Driven Clustering". ICML 2006.

Neil D. Lawrence, Joaquin Quiñonero-Candel. Local Distance Preservation in the GP-LVM through Back Constraints. ICML 2006.

2006-07-10 ICML 2006 feedback session

Chris Williams
Pascal Poupart, Nikos Vlassis, Jesse Hoey, Kevin Regan. An Analytic Solution to Discrete Bayesian Reinforcement Learning.

Stefan Harmeling
Charles Elkan. Clustering Documents with an Exponential-Family Approximation of the Dirichlet Compound Multinomial Distribution.

Edwin Bonilla
David M. Blei, John D. Lafferty. Dynamic Topic Models.

2006-05-29 Charles Fox

A Ekert, P Hayden, H Inamori. Basic concepts in quantum computation.

2006-05-15 Amos Storkey

R. Rosales and T. Jaakkola. Focused Inference. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.

Martijn A. R. Leisink and Hilbert J. Kappen, Bound Propagation. Journal of Artificial Intelligence Research 19 (2003), pp. 139-154.

2006-04-24 Edwin Bonilla

David Wolpert. Predictive Game Theory. Workshop on Game Theory, Machine Learning and Reasoning under Uncertainty, at NIPS 2005.

2006-03-27 Marc Toussaint

Adnan Darwiche. A Differential Approach to Inference in Bayesian Networks. In Journal of the ACM, Vol. 50, No. 3, pages 280-305, 2003.

2006-03-13 Stefan Harmeling

A. Pfeffer. IBAL: An Integrated Bayesian Agent Language. IJCAI 2001.

B. Milch, B. Marthi, S. Russell, D. Sontag, D. L. Ong, and A. Kolobov. BLOG: Probabilistic Models with Unknown Objects. IJCAI 2005.

2006-02-27 Tom Griffiths

N. Friedman, L. Getoor, D. Koller and A. Pfeffer. Learning Probabilistic Relational Models. Proc. IJCAI 1999, pp. 1300-1307.

D. Heckerman, C. Meek, and D. Koller. Probabilistic Models for Relational Data. Technical Report MSR-TR-2004-30, Microsoft Research, March 2004.

2006-02-13 NIPS feedback session

Michael I. Jordan. Dirichlet processes, Chinese restaurant processes and all that. Tutorial, NIPS 2005.

2006-01-30 NIPS feedback session

Amos Storkey
Boaz Nadler, Stephane Lafon, Ronald Coifman, Ioannis Kevrekidis. Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators.

Stefan Harmeling
Tom Griffiths, Zoubin Ghahramani. Infinite latent feature models and the Indian buffet process.

Edwin Bonilla
David Blei and John Lafferty. Correlated Topic Models.

2006-01-23 NIPS feedback session

John Quinn
David Fleet, Jack Wang, and Aaron Hertzmann. Gaussian Process Dynamical Models.

Felix Agakov
Brigham Anderson and Andrew Moore. Efficient Value of Information for Graphical Models.

Moray Allan
Sharon Goldwater, Thomas L. Griffiths, and Mark Johnson. Interpolating Between Types and Tokens by Estimating Power-Law Generators.

Marc Toussaint
Deepak Verma and Rajesh P. N. Rao. Goal-Based Imitation as Probabilistic Inference over Graphical Models.

2005-11-21 Felix Agakov

Francis R. Bach and Michael I. Jordan. Predictive low-rank decomposition for kernel methods. ICML 2005.

Katherine A. Heller and Zoubin Ghahramani. Bayesian Hierarchical Clustering. ICML 2005.

2005-10-31 Wolfgang Lehrach

Kengo Sato and Yasubumi Sakakibara. RNA secondary structural alignment with conditional random fields. ECCB 2005.

Henry C. M. Leung and Francis Y. L. Chin. Finding exact optimal motifs in matrix representation by partitioning. ECCB 2005.

2005-10-17 Marc Toussaint

Tao Wang and Daniel Lizotte and Michael Bowling and Dale Schuurmans. Bayesian Sparse Sampling for On-line Reward Optimization. ICML 2005.

Anders Jonsson and Andrew Barto. A Causal Approach to Hierarchical Decomposition of Factored MDPs. ICML 2005.

2005-10-03 Amos Storkey

Hinton, G. E., Osindero, S. and Teh, Y. A fast learning algorithm for deep belief nets. Submitted to Neural Computation.

2005-09-12 UAI feedback session

Wolfgang Lehrach
Uri Nodelman, Christian R. Shelton and Daphne Koller. Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks. Proc. UAI 2005, pp. 421-430.

Felix Agakov
Pieter Abbeel, Daphne Koller and Andrew Y. Ng. Learning factor graphs in polynomial time & sample complexity. Proc. UAI 2005, pp. 1-9.

Chris Williams
Michal Rosen-Zvi, Michael I. Jordan and Alan L. Yuille. The DLR Hierarchy of Approximate Inference. Proc. UAI pp. 493-500.

2005-08-22 UAI feedback session

Amos Storkey
M. Welling, T. Minka and Y.W. Teh. Structured Region Graphs: Morphing EP into GBP. UAI 2005.

Chris Williams
Charles Sutton and Andrew McCallum . Piecewise Training for Undirected Models. UAI 2005.

Felix Agakov
Joris M. Mooij and Hilbert J. Kappen. Sufficient Conditions for Convergence of Loopy Belief Propagation. UAI 2005.

2005-06-20 Tom Griffiths

J. Zhang, Z. Ghahramani, Y. Yang. A Probabilistic Model for Online Document Clustering with Application to Novelty Detection. NIPS 2004.

2005-05-09 David Barber

Expectation Correction for smoothing in Switching Linear Gaussian State Space models. JMLR (submitted).

2005-04-19 Chris Williams

M. A. O. Vasilescu, D. Terzopoulos. Multilinear Analysis of Image Ensembles: TensorFaces. Proc. 7th European Conference on Computer Vision (ECCV'02), Copenhagen, Denmark, May, 2002, in Computer Vision – ECCV 2002, Lecture Notes in Computer Science, Vol. 2350, A. Heyden et al. (Eds.), Springer-Verlag, Berlin, 2002, 447-460.

D. Grimes, R. Rao. Bilinear sparse coding for invariant vision. Neural Computation, 17(1), 47-73, 2005.

2005-03-21 Amos Storkey

W. D. Penny, K. E. Stephan, A. Mechelli, K. J. Friston. Modelling Functional Integration: A Comparison of Structural Equation and Dynamic Causal Models. NeuroImage 23:S264-274, 2004.

2005-03-07 Edwin Bonilla

Kenji Fukumizu, Francis R. Bach, Michael I. Jordan. Kernel Dimensionality Reduction for Supervised Learning. NIPS 2003.

Yee Whye Teh, Matthias Seeger, Michael I. Jordan. Semiparametric latent factor models. AISTATS 2004.

2005-02-21 Marc Toussaint

Marc Toussaint. Compact genetic codes as a search strategy of evolutionary processes. Foundations of Genetic Algorithms 8, 2005.

Shumeet Baluja and Scott Davies. Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space. ICML 1997.

2005-02-07 NIPS feedback session

Wolfgang Lehrach
Elizaveta Levina and Peter J. Bickel. Maximum Likelihood Estimation of Intrinsic Dimension. NIPS 2004.

John Quinn
Francis R. Bach and Michael I. Jordan. Blind One-microphone Speech Separation: A Spectral Learning Approach. NIPS 2004.

Moray Allan
Pierre Moreels and Pietro Perona. Common-Frame Model for Object Recognition. NIPS 2004.

Chris Williams
Changjiang Yang, Ramani Duraiswami and Larry Davis. Efficient Kernel Machines Using Fast Gauss Transform. NIPS 2004.

2005-01-24 NIPS feedback session

Chris Williams
E. Doi and M. S. Lewicki. Sparse coding of natural images using an overcomplete set of limited capacity units. NIPS 2004.

Moray Allan
Thomas L. Griffiths, Mark Steyvers, David M. Blei and Joshua B. Tenenbaum. Integrating Topics and Syntax. NIPS 2004.

John Quinn
Alexei Efros, Volkan Isler, Jianbo Shi and Mirko Visontai. Seeing through water. NIPS 2004.

Wolfgang Lehrach
Dustin Lang and Nando de Freitas. Beat Tracking the Graphical Model Way. NIPS 2004.

2004-12-07 Amos Storkey

Parag and Pedro Domingos. Multi-Relational Record Linkage. KDD-2004 Workshop on Multi-Relational Data Mining, p. 31-48, 2004.

2004-11-02 Chris Williams

R. Vidal, Y. Ma, J. Piazzi. A new GPCA algorithm for clustering subspaces by fitting, differentiating and dividing polynomials. CVPR vol. I, 2004.


Wolfgang Lehrach
David J. Reiss and Benno Schwikowski. Predicting protein-peptide interactions via a network-based motif sampler. ISMB 2004, Proceedings 20 suppl. 1 2004, p. i274-i282.

Dirk Husmeier
Jun S. Liu, Andrew F. Neuwald, and Charles E. Lawrence. Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. Journal of the American Statistical Association, 90(432) p. 1156-1170, December 1995.

2004-09-21 Felix Agakov

Adrian Corduneanu, Tommi Jaakkola. On Information Regularization. UAI 2003.

Iftach Nachman, Gal Elidan, and Nir Friedman. "Ideal Parent" Structure Learning for Continuous Variable Networks. UAI 2004.

2004-09-07 John Quinn

Nando de Freitas, Richard Dearden, Frank Hutter, Ruben Morales-Menendez, Jim Mutch and David Poole. Diagnosis by a waiter and a Mars explorer. Proceedings of the IEEE, 92(3), 2004.

2004-08-24 Michael Schouten

A. Morris, J. Whittaker and D. Balding. Bayesian fine-scale mapping of disease loci, by hidden Markov models. Am. J. Hum. Genet., 67, 155–169, 2000.

2004-08-10 CVPR feedback session

Chris Williams
Nebojsa Jojic and Yaron Caspi. Capturing image structure with probabilistic index maps.

Michalis Titsias
Pedro Felzenszwalb and Daniel Huttenlocher. Efficient belief propagation for early vision.

Moray Allan
Kilian Weinberger and Lawrence Saul. Unsupervised learning of image manifolds by semidefinite programming.

2004-07-27 Hanna Wallach

Yuan Qi, Thomas P. Minka, Rosalind W. Picard, and Zoubin Ghahramani. Predictive Automatic Relevance Determination by Expectation Propagation. ICML 2004.

2004-06-29 Felix Agakov

Michael Kearns. A Tutorial on Computational Game Theory. NIPS 2002 tutorial.

M. Kearns, M. Littman, S. Singh. Graphical Models for Game Theory. In Proceedings of UAI 2001.

Pierfrancesco La Mura. Game Networks. UAI 2000.

2004-06-15 Amos Storkey

Lyndsey C. Pickup, Stephen J. Roberts and Andrew Zisserman. A sampled Texture Prior for Image Super-Resolution. NIPS 2003.

2004-05-04 Yuval Krymolowski

E. Gassiat, S. Boucheron. Optimal error exponents in hidden Markov model order estimation. IEEE Trans. Info. th., 48, 864-880, April 2003.

2004-04-06 Amos Storkey

C. Sminchisescu, M Welling and G. Hinton. A Mode-hopping MCMC sampler

2004-03-23 Michalis Titsias

David Wipf, Bhaskar Rao. Perspectives on Sparse Bayesian Learning, NIPS 2003.

Noam Shental, Assaf Zomet, Tomer Hertz, Yair Weiss. Pairwise Clustering and Graphical Models. NIPS 2003.

2004-03-02 Louis Attalah

Fergus, R. , Perona, P. and Zisserman, A. Object Class Recognition by Unsupervised Scale-Invariant Learning, Proc. of the IEEE Conf on Computer Vision and Pattern Recognition, 2003.

2004-02-17 Felix Agakov

Tom Heskes, Kees Albers and Bert Kappen. Approximate Inference and Constained Optimization, UAI 2003.

Amir Globerson, Gal Chechik and Naftali Tishby. Sufficient Dimensionality Reduction with Irrelevance Statistics, UAI 2003.

2004-02-03 NIPS feedback session

Francis R. Bach, Michael I. Jordan Learning Spectral Clustering. NIPS 2003.

Radford M. Neal, Matthew J. Beal, Sam T. Roweis Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models. NIPS 2003.

David Donoho, Victoria Stodden When Does Non-Negative Matrix Factorization Give a Correct Decomposition Into Parts? NIPS 2003.

Mark Girolami, Ata Kaban Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles. NIPS 2003.

2004-01-20 NIPS feedback session

Ben Taskar, Carlos Guestrin, Daphne Koller. Max-Margin Markov Networks. NIPS 2003.

Martin J. Wainwright, Michael I. Jordan Semidefinite Relaxations for Approximate Inference on Graphs with Cycles. NIPS 2003.

Kevin Murphy, Antonio Torralba, William T. Freeman Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes. NIPS 2003.

Neil D. Lawrence Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. NIPS 2003.

2003-11-11 Sethu Vijayakumar

M.J. Beal, N. Jojic and H. Attias. A graphical Model for Audiovisual Object Tracking, PAMI, 25, July 2003.

2003-10-28 Chris Williams

Michael Isard. Pampas: Real-Valued Graphical Models for Computer Vision CVPR, vol. 1 613-620, 2003.

Erik B. Sudderth, Alexander T. Ihler, William T. Freeman, and Alan S. Willsky. Nonparametric Belief Propagation, CVPR, 2003.

2003-02-25 Felix Agakov

M. Hauskrecht. Value-function approximations for partially observable Markov decision processes.

Kevin P. Murphy. Bayesian Map Learning in Dynamic Environments. In NIPS-99.

2003-02-18 David Barber

Kevin Murphy's report: A Survey of POMDP Solution Techniques.

Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra. Planning and acting in partially observable stochastic domains.

2003-01-28 Matthias Seeger

Wainwright et al: A New Class of Upper Bounds on the Log Partition Function, UAI 2002.

Wainwright et al: Exact MAP Estimates by (Hyper)Tree Agreement, NIPS 2003 (preprint).

Heskes and Zoeter. Generalized Belief Propagation for Approximate Inference in Hybrid Bayesian Networks.

2002-12-03 Amos Storkey

Alexander G. Gray, Bernd Fischer, Johann Schumann, and Wray Buntine: Automatic Derivation of Statistical Algorithms: The EM Family and Beyond.

Carl Edward Rasmussen and Zoubin Ghahramani: Bayesian Monte Carlo.

Doudou LaLoudouana and Mambobo Bonouliqui Tarare: Data Set Selection. (Winner of the Award "Most original submission" at NIPS 2002!)

2002-11-19 Michalis Titsias

Chris Bishop. Sampling Methods.

2002-11-05 Chris Williams

Factorial HMMs and the Generalized Backfitting Algorithm.

Dependency Networks for Inference, Collaborative Filtering, and Data Visualization.


Taylan Cemgilm. MAP Estimation in Switching State Space Models

-- Main.imurray2 - 02 Dec 2011

Topic revision: r1 - 02 Dec 2011 - 10:35:16 - Main.imurray2
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