Machine Learning/AI Reading Group

• Co-ordinator: Soumya Ray (email sray_AT_case if you'd like to join the mailing list)
• Meeting Time and Location: Olin 709, Th 12-2 every week (usually 12-1, extra for especially interesting papers)

Current Papers

• (Nov 19, Dec 3) Ulrike von Luxburg (2007). A Tutorial on Spectral Clustering. Statistics and Computing, vol 17 no. 4,pp 395--416.
• (Dec 3) Andrew Ng, Michael Jordan and Yair Weiss (2001). On Spectral Clustering: Analysis and an Algorithm. In the 15th Advances in Neural Information Processing Systems, Vancouver BC, Canada.

Future Papers

• Marina Meila (2006). The Uniqueness of a good optimum for k-means. In Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.
• Arindam Banerjee et al (2005). Clustering with Bregman Divergences. Journal of Machine Learning Research, vol 6, pp 1705--1749.
• Huma Lodhi et al (2002). Text Classification using String Kernels. Journal of Machine Learning Research, vol 2, pp 419--444.
• Olivier Chapelle et al (2008). Optimization techniques for semi-supervised support vector machines. Journal of Machine Learning Research, vol 9, pp 203--233.
• Rie K. Ando and Tong Zhang (2005). A Framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, vol 6, pp 1817--1853.
• Ryan Adams et al (2009). Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities. Proceedings of the 26th International Conference on Machine Learning, Montreal, Quebec, CA.
• Adrian Dobra (2009). Variable Selection and dependency networks for genomewide data. Biostatistics.
• Trevor Hastie et al (2004). The entire regularization path for the support vector machine. Journal of Machine Learning Research, vol 5, pp 1391-1415.
• Andrew Gelman et al (2007). Rich State, Poor State, Red State, Blue State: Whats the Matter with Connecticut? Quarterly Journal of Political Science, vol 2, pp 345--367.
• David J. Hand (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning, vol 77 no. 1, pp 103--123.
• David Blei et al (2004). Hierarchical Topic Models and the Nested Chinese Restaurant Process. In the 18th Advances in Neural Information Processing Systems (NIPS), Vancouver BC, Canada.
• Thomas G. Dietterich et al (2008). Gradient Tree Boosting for Training Conditional Random Fields. Journal of Machine Learning Research, vol 9, pp 2113-2139.
• T. Finley and T. Joachims (2005). Supervised Clustering with Support Vector Machines. In Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.
• T. Heskes (2000). Empirical Bayes for Learning to Learn. In Proceedings of the 17th International Conference on Machine Learning, Stanford, CA, USA.
• Naftali Tishby et al (1999). The Information Bottleneck Method. In Proceedings of the 37th annual Allerton Conference on Communication, Control, and Computing, pp. 368--377.
• Gal Chechik et al (2005). Information Bottleneck for Gaussian Variables. Journal of Machine Learning Research 6, pp. 165--188.
• Susanne Still et al (2004). Geometric Clustering Using the Information Bottleneck Method. In Advances in Neural Information Processing Systems 16, Vancouver, BC, Canada.

Past Papers

• (Jan 21) Nachum Dershowitz and Yuri Gurevich (2008). A natural axiomatization of computability and proof of Church's Thesis. Bulletin of Symbolic Logic, Volume 14, Issue 3, pp299-350.
• (Feb 4) Vikas Raykar et al (2008). Bayesian Multiple Instance Learning. In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.
• (Feb 4) Sham M. Kakade et al (2008). Efficient Bandit Algorithms for Online Multiclass Prediction. In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.
• (Feb 16) Leslie Kaelbling et al (1996). Reinforcement Learning: A survey. Journal of AI Research, vol 4, pp 237--285.
• (Feb 23) Kenji Doya (2000). Reinforcement Learning in Continuous Time and Space. Neural Computation, vol 12, no. 1, pp 219--245. Background 1, Background 2, Background 3
• (March 2) Eric Xing, Michael Jordan and Richard Karp (2001). Feature Selection for High-dimensional Microarray Data. In Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA, USA.
• (March 16) Daphne Koller and Mehran Sahami (1996). Towards optimal feature selection. Technical Report, Computer Science Department, Stanford University.
• (March 23) Koby Crammer et al (2006). Online Passive Aggressive Algorithms. Journal of Machine Learning Research, vol 7, pp 551--585.
• (March 30) Joshua Tenenbaum et al (2000). A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, vol 290, pp 2319-2323.
• (April 6) Philip Dixon et al (2005). Improving the precision of estimates of the frequency of rare events. Ecology, vol 86 no 5, pp 1114-1123.
• (April 13) Tom Dietterich (2000). Hierarchical Reinforcement Learning with the MAXQ value function decomposition. Journal of AI Research, vol 13, pp 227--303.
• (April 20) Rajat Raina et al (2003). Classification with Hybrid Generative/Discriminative Models. Proceedings of Seventeenth Annual conference on Neural Information Processing Systems, Vancouver BC, Canada.
• (April 27) John Hardy and Andrew Singleton (2009). Genomewide Association Studies and Human Disease. New England Journal of Medicine, vol 360 pp 1759--1768.
• (April 27) John Storey and Robert Tibshirani (2003). Statistical Significance of genomewide studies. PNAS, vol 100 no 16, pp 9440--9445.
• (May 4) David Mease and Abraham Wyner (2008). Evidence contrary to the statistical view of boosting. Journal of Machine Learning Research, vol 9, pp 131--156.
• (June 29) Honglak Lee et al (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th International Conference on Machine Learning, Montreal, Quebec, CA.
• (July 6) Linli Xu et al (2009). Optimal Reverse Prediction: A unified Perspective on Supervised,Unsupervised and Semi-Supervised Learning. Proceedings of the 26th International Conference on Machine Learning, Montreal, Quebec, CA.
• (July 20) Han Liu et al (2009). Blockwise Coordinate Descent Procedures for the multi-task Lasso, with Applications to Neural Semantic Basis Discovery. Proceedings of the 26th International Conference on Machine Learning, Montreal, Quebec, CA.
• (July 28) Su-In Lee et al (2006). Efficient Structure Learning of Markov Networks using L1-Regularization. In Proceedings of the 20th Conference on Neural Information Processing Systems, Vancouver BC, Canada.
• (August 25/Sept 3) Tommi S. Jaakkola (2000). Tutorial on variational approximation methods. Slides. In Advanced Mean Field Methods: Theory and Practice.
• (August 11/18, Sept 3) David Blei et al (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, vol 3, pp 993--1022.
• (Sept 10) Ross D. King et al (2009). The Automation of Science. Science vol 324, pp 85--89.
• (Sept 17) Kenneth A. Norman et al (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, vol 10, no. 9, pp 424--430.
• (Sept 24) Rebecca Hutchinson et al (2006). Hidden Process Models. Proceedings of the 26th International Conference on Machine Learning, Pittsburgh, PA, USA.
• (Oct 1) Jerome H. Friedman (1999). Stochastic Gradient Boosting. Technical Report, Stanford University, CA, USA.
• (Oct 8/15) Bradley Efron (2008). Microarrays, Empirical Bayes and the Two-Groups Model. Statistical Science, vol 23 no. 1, pp 1-22. Comment 1 Comment 2 Comment 3 Comment 4 Rejoinder
• (Oct 29) Yoav Benjamini and Yosef Hochberg (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B Methodological, v. 57 issue 1, pp. 289-300.
• (Oct 29) Hemant Ishwaran and J. Sunil Rao (2003). Detecting Differentially Expressed Genes in Microarrays Using Bayesian Model Selection. Journal of the American Statistical Association June 2003, Vol. 98, No. 462, Theory and Methods, pp. 438--455.
• (Nov 5) Alexander L. Cohen at al (2008). Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage v41, pp 45--57.
• (Nov 5) Damien Fair et al (2008). The maturing architecture of the brain's default network. PNAS, vol 105 no. 10, pp 4028--4032.