Tommi's Reading Group

Place: 32-G451
Time: Thursday 5pm

Next week's paper(s):
T. Richardson and R. Urbanke, "The capacity of low-density parity-check codes under message-passing decoding," IEEE Trans. Inform. Theory, Feb. 2001. pdf

Organizer: Tommi Jaakkola

Past papers:
11/9/06Chu et al. "Relational learning with Gaussian processes". Advances in NIPS 19, 2007. pdf
11/2/06Yee Whye Teh, Dave Newman and Max Welling. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation. Advances in NIPS 19, 2007. ps
10/5/06Martin Wainwright. Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting. JMLR volume 7, pages 1829--1859, 2006. pdf
9/28/06Elitza Maneva, Elchanan Mossel, and Martin J. Wainwright. A New Look at Survey Propagation and its Generalizations. 2004. Technical Report 669, Department of Statistics, University of California, Berkeley. ps
8/16/06Yuri Rabinovich, Alistair Sinclair and Avi Wigderson. Quadratic Dynamical Systems (Preliminary Version). Proceedings of Foundations of Computer Science (FOCS), pp. 304-313, 1992. ps
6/2/06Lieberman E, C Hauert, MA Nowak (2005). Evolutionary Dynamics on Graphs, Nature 433:312-316. pdf
5/26/06Kui-Yu Chang; Ghosh, J. A unified model for probabilistic principal surfaces IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 23 (1), 2001. pdf
5/12/06Shuo-Yen Robert Li, Raymond W. Yeung and Ning Cai. Linear Network Coding. IEEE Transactions on Information Theory. 2003. pdf
5/5/06Vijay Balasubramanian. Statistical Inference, Occam's Razor, and Statistical Mechanics on the Space of Probability Distributions. 1997. pdf
4/21/06Mathias Drton and Bernd Sturmfels and Seth Sullivant. Algebraic Factor Analysis: Tetrads, Pentads and Beyond. 2005. ps
3/24/06F. Sha and L. K. Saul (2005). Analysis and extension of spectral methods for nonlinear dimensionality reduction. In Proceedings of the Twenty Second International Conference on Machine Learning (ICML-05), pages 785-792. ps
3/10/06Stephen Boyd. Convex Optimization of Graph Laplacian Eigenvalues. To appear, Proceedings of International Congress of Mathematics. Volume 3. 2006. pdf
5/23/05Andreas Krause and Carlos Guestrin. Near-optimal nonmyopic value of information in graphical models. UAI 2005 pdf
5/9/05Kass & Steffey, Approximate Bayesian inference in conditionally independent hierarchical models, JASA Vol 84, 717--726, 1989. pdf
4/12/05S. Hochreiter and K. Obermayer. Optimal Kernels for Unsupervised Learning, 2005 ps
4/5/05Ikeda, Tanaka, and Amari, "Stochastic Reasoning, Free Energy, and Information Geometry", Neural Computation, 16, 1779-1810. 2004. pdf
2/8/05Jennifer Listgarten and Radford Neal, Sam Roweis and Andrew Emili. Multiple Alignment of Continuous Time Series. NIPS*04. pdf
11/29/04S. Dasgupta. Analysis of a greedy active learning strategy. NIPS*04. ps
Semi-Markov Conditional Random Fields for Information Extraction. Sunita Sarawagi, William Cohen. NIPS*04. pdf
11/22/04Sajama Sajama and Alon Orlitsky. Semi-parametric exponential family PCA : Reducing dimensions via non-parametric latent distribution estimation. NIPS*04. ps
d'Aspremont, A., El Ghaoui, L., Jordan, M.I., Lanckriet, G.R.G. (2004). A Direct Formulation for Sparse PCA using Semidefinite Programming. NIPS*04. pdf
11/15/04Jean-Philippe Vert adn Yoshihiro Yamanishi. Supervised graph inference. NIPS*04. pdf
Dengyong Zhou, Bernhard Schoelkopf, Thomas Hofmann. Semi-supervised Learning on Directed Graphs. NIPS*04. pdf
11/8/04 Jonathan Baxter. A Bayesian/information theoretic model of learning to learn via multiple task sampling. Machine Learning, 28(1):7--39, Jul 1997. pdf
11/1/04 Gilad-Bachrach, Navot, and Tishby, "Bayes and Tukey Meet at the Center Point", COLT 2004. pdf
10/18/04 Noam Shental and Assaf Zomet and Tomer Hertz and Yair Weiss. Pairwise Clustering and Graphical Models. NIPS 2003. pdf
Andrew McCallum and Ben Wellner. Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference. Proceedings of the IJCAI Workshop on Information Integration on the Web. 2003. pdf
10/4/04 S. Dasgupta and A. Gupta. An elementary proof of a theorem of Johnson and Lindenstrauss. Random Structures and Algorithms, 22(1):60-65, 2003. pdf
9/27/04 Dimitris Bertsimas and Santosh Vempala. Solving convex programs by random walks. Journal of the ACM (JACM) 51(4), 540--556, 2004. pdf
Maria-Florina Balcan, Avrim Blum and Santosh Vempala. On Kernels, Margins and Low-dimensional Mappings. pdf
9/14/04 Teh, Jordan, Beal and Blei. "Hierarchical Dirichlet Processes". 2004. pdf
5/3/04 Pachter and Sturmfels, "Parametric inference for Biological Sequence Analysis" pdf
2/13/04An Alternate Objective Function for Markovian Fields. Sham Kakade, Yee Whye Teh & Sam Roweis. International Conference on Machine Learning 19 (ICML'02). pp. 275--282. ps
D.P. de Farias and N. Megiddo, "How to Combine Expert (or Novice) Advice when Actions Impact the Environment." NIPS 16. pdf
11/20/03Zhu, Lafferty, Ghahramani. Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining. ps
Zhu, Ghahramani, Lafferty. Semi-supervised learning using Gaussian fields and harmonic functions. ICML 2003. ps
10/30/03Beal, Ghahramani, Rasmussen, "The Infinite Hidden Markov Model". NIPS 14. 2003 pdf
10/16/03Gilad-Bachrach, Navot and Tishby. An Information Theoretic Tradeoff Between Complexity and Accuracy. COLT'
10/2/03Hierarchical topic models and the nested Chinese restaurant process. D. M. Blei, T. Griffiths, M. I. Jordan, and J. Tenenbaum. NIPS 16, 2004. pdf
9/26/03Max-Margin Markov Networks. B. Taskar, C. Guestrin and D. Koller. NIPS 16. 2004. ps
5/7/03 Lafferty and Lebanon. Information diffusion kernels. NIPS 15.
3/21/03Meir Feder and Neri Merhav, "Hierarchical Universal Coding", IEEE Transactions on Information Theory, Vol. 42, No. 5, 1996pdf
3/14/03 Bayarri, M. J. and Berger, J. (1998) Robust Bayesian analysis of selection models. Annals of Statistics (26), 645-659. ps
3/7/03David McAllester. PAC-Bayesian Stochastic Model Selection.
2/21/03Donald B. Rubin, Inference and Missing Data, Biometrika, Vol. 63, No. 3. (Dec., 1976), pp.
Daniel F. Heitjan; Donald B. Rubin, Ignorability and Coarse Data, Annals of Statistics, Vol. 19, No. 4. (Dec., 1991), pp.
1/23/03Generalized^2 Linear^2 Models by Geoff Gordon. NIPS*
Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart Russell: Distance Metric Learning, with application to Clustering with side-information. NIPS*
12/4/02Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, Mikhail Belkin and Partha Niyogi, Technical Report, CS Department, Univ. of Chicago.
Jonathan Q. Li, Andrew R. Barron, Mixture Density Estimation (1999)ps
11/18/02Even more NIPS Papers!
11/13/02More NIPS Papers.
11/6/02NIPS Papers.
10/28/02Thomas A. Severini, Wing Hung Wong Profile Likelihood and Conditionally Parametric Models Annals of Statistics, Vol. 20, No. 4. (Dec., 1992), pp. 1768-1802.pdf
10/21/02The geometry of mixture likelihoods: a general theory. Annals of Statistics, 11, 86-95.pdf
10/9/02F. Sha, L. K. Saul, and D. D. Lee, Multiplicative updates for nonnegative quadratic programming in support vector machines, 2002pdf
9/25/02Globerson and Tishby. Sufficient dimensionality reduction. ps
7/22/02Berger and Guglielmi. Bayesian testing of a parametric model versus non-parametric alternatives. 1999. Technical Report. ps
7/15/02Jorma Rissanen. Stochastic Complexity and Modeling. Annals of Statistics, Vol. 14, No. 3. (Sep., 1986), pp. 1080-1100pdf
6/24/02A. P. Dawid. Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach. Journal of the Royal Statistical Society. Series A (General), Vol. 147, No. 2, (1984), pp. 278-292pdf
6/17/02Schwarz, Estimating the dimension of a model. Annals of Statistics, 6, 1978.pdf
6/3/02Rosenthal. A class of games possessing pure-strategy Nash equilibria. International Journal of Game Theory, 2:65 67, 1973.
Dov Monderer, Lloyd S. Shapley. Potential Games, pp. 124-143, 1996pdf
5/13/02Efficient Nash Computation in Large Population Games with Bounded Influence. M Kearns and Y Mansour. To appear, Proceedings of UAI 2002.pdf
5/6/02Chapters 3-4 of Osborne and Rubinstein, "A course in Game Theory."
4/22/02Chapters 1-2 of Osborne and Rubinstein, "A course in Game Theory."
4/8/02Kalai and Lehrer. Rational Learning and Nash Equilibrium. Econometrica. 1993. pdf
3/18/02Learning the kernel matrix with semidefinite programming. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, M. I. Jordan. pdf
3/4/02"Weighted Low-Rank Approximation to General Complex Matrices and Its Application in the Design of 2D Digital Filters", Lu, Pei, Wang, IEEE Trans on Circuits and Systems I, 44(7):650-655.pdf
2/25/02T. Cover. Universal Portfolios. Mathematical Finance, 1(1): 1-29, January
2/11/02Yoav Freund and Manfred Opper. Continuous Drifting Games.
1/28/02Llew Mason, Jonathan Baxter, Peter Bartlett and Marcus Frean. Boosting Algorithms as Gradient Descent. Neural Information Processing Systems.
F. d'Alche-Buc, Yves Grandvalet and Christophe Ambroise. Semi-supervised MarginBoost. Neural Information Processing Systems.
1/14/02 Kernel independent component analysis. F. Bach and M. I. Jordan. 2001. Technical Report UCB/CSD-01-1166. ps
11/26/01 Thomas S. Ferguson. A Bayesian Analysis of Some Nonparametric Problems Annals of Statistics, Vol. 1, No. 2. (Mar., 1973), pp. 209-230. ps
11/19/01 D. Blei, A. Y. Ng and M. I. Jordan. Latent Dirichlet allocation. Advances in Neural Information Processing Systems 14, 2002. ps
10/29/01 S. Tong and D. Koller. Active Learning for Structure in Bayesian Networks. Seventeenth International Joint Conference on Artificial Intelligence, Seattle, Washington, August 2001, pages 863--869. ps
10/22/01 Yevgeny Seldin, Gill Bejerano and Naftali Tishby. Unsupervised Sequence Segmentation by a mixture of Variable Memory Markov Models. 2001. ICML-01. ps
10/1/01 Michael I. Jordan and Christopher M. Bishop. An Introduction to Graphical Models. Unpublished manuscript. 2001. Chapters 19 & 20.
Do not distribute these book chapters.
9/24/01 John Lafferty, Andrew McCallum and Fernando Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML-2001. ps
9/17/01 Thomas P. Minka. A family of algorithms for approximate Bayesian inference. Ph.D. Thesis, Massachusetts Institute of Technology. 2001. ps
9/10/01 J.S. Yedidia, W.T. Freeman, Y. Weiss. Understanding Belief Propagation and its Generalizations. To appear as a chapter in the IJCAI 2001 volume of distinguished lectures. pdf
8/20/01 Andrew Ng, Alice Zheng and Michael Jordan. Link Analysis, Eigenvectors and Stability. IJCAI-01. ps
8/13/01 Adrian Corduneanu and Tommi Jaakkola. Stable Mixing of Complete and Incomplete Information. NIPS-01.
7/30/01 Shun-ichi Amari. 2001. Information geometry on hierarchy of probability distributions. IEEE Transactions on Information Theory, vol. 47, no. 5. pdf
7/16/01 Noam Slonim, Nir Friedman, Naftali Tishby. Submitted. Agglomerative Multivariate Information Bottleneck. ps
7/9/01 Radford M. Neal. 2001. Defining priors for distributions using dirichlet diffusion trees. Technical Report No. 0104, Department of Statistics, University of Toronto. ps
5/14/01 M. Kearns, M. Littman, S. Singh. 2001. Graphical models for game theory. Uncertainty in Artificial Intelligence. ps
5/7/01 Nir Friedman, Ori Mosenzon, Noam Slonim and Naftali Tishby. Multivariate Information Bottleneck. Submitted to UAI-2001. ps
4/9/01 T. P. Speed and H. T. Kiiveri. 1986. Gaussian markov distributions over finite graphs. Annals of Statistics, Vol. 14, No. 1, 138-150. pdf
3/19/01 3/12/01 Shun-ichi Amari. Information Geometry of the EM and em Algorithms for Neural Networks. Neural Networks, Vol.8, No. 9, pp. 1379-1408, 1995. pdf
3/5/01 Csiszar and Tusnady (1984). Information geometry and alternating minimization procedures. Statistics and Decisions No. 1, 205-237.
2/26/01 Gareth James and Trevor Hastie (2000). Functional Linear Discriminant Analysis for Irregularly Sampled Curves. Journal of the Royal Statistical Society, Series B (Under review). ps
2/19/01 B. North, A. Blake, M. Isard, and J. Rittscher, Learning and Classification of Complex Dynamics, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 9, September 2000, pp. 1016-1034. pdf
12/18/00 Lawrence Saul and Mazin Rahim. Markov Processes on Curves. Machine Learning. 2000. Vol. 41, pp. 345-363. ps
11/20/00 Cox and Cox. Multidimensional scaling.
11/13/00 Thomas Martinetz and Klaus Schulten. Topology Representing Networks. Neural Networks. 1994. Vol. 7, No. 3, pp. 507-522. pdf
11/6/00 Sam Roweis and Lawrence Saul. Non-linear dimensionality reduction by locally linear embedding. 2000. Science. Vol. 290, pp. 2323-2326. pdf
10/30/00 Joshua Tenenbaum. Mapping a manifold of perceptual observations. Advances in Neural Information Processing Systems 10. 1998. ps
10/23/00 M. Collins, R. Schapire and Y. Singer. Logistic Regression, AdaBoost and Bregman Distances. Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT-00). 2000. ps
10/16/00 Scholkopf, et. al. Estimating the Support of a High-Dimensional Distribution. Microsoft Technical Report, MSR-TR-99-87. 1999. ps
? Johnathan Baxter. Learning Internal Representations. Proceedings of the 8th Annual Conference on Computational Learning Theory, pp. 311-320, 1995. ps
? Thomas Hofmann. Probabilistic Latent Semantic Analysis. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 1999. ps
? Noam Slonim and Naftali Tishby. Agglomerative Information Bottleneck.. Advances in Neural Information Processing Systems 12, 2000. ps

| Last modified: Wed Nov 15 19:04:10 2006