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: Glennan 519C, W 2-3 every week

Current Paper

• Alona Fyshe et al (2015). A Compositional and Interpretable Semantic Space. NAACL.

Past Papers

• (Jan 21 2009) 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 23rd 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.
• (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.
• (Dec 10) 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.
• (Dec 17) Tijl De Bie and Nello Cristianini (2006). Fast SDP Relaxations of Graph Cut Clustering, Transduction,and Other Combinatorial Problems. Journal of Machine Learning Research, vol 7, pp 1409--1436.
• (Jan 7 2010) T. Heskes (2000). Empirical Bayes for Learning to Learn. In Proceedings of the 17th International Conference on Machine Learning, Stanford, CA, USA.
• (Jan 13) 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.
• (Jan 20) T. Finley and T. Joachims (2005). Supervised Clustering with Support Vector Machines. In Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.
• (Jan 27) A. Azran and Z. Ghahramani (2006). Spectral Methods for Automatic Multiscale Data Clustering. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, USA.
• (Feb 3) D. Lowd and P. Domingos (2008). Learning Arithmetic Circuits. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, Helsinki, Finland.
• (Feb 10) L. Zelnik-Manor and P. Perona (2004). Self-tuning spectral clustering. In Proceedings of the Eighteenth Conference on Neural Information Processing Systems, Vancouver, BC.
• (Feb 17) M. Lewicki. Independent Component Analysis.
• (Feb 24) P. Domingos and M. Pazzani (1997). On the optimality of the simple Bayesian Classifier under zero-one loss. Machine Learning, vol 29 nos 2-3, pp 103--130.
• (March 3) J. Glascher et al (2010). Distributed neural system for general intelligence revealed by lesion mapping. PNAS (early edition, published online).
• (Mar 10) S. Smith et al (2009). Correspondence of the brain's functional architecture during activation and rest. PNAS, vol 106 no 31, pp 13040-13045.
• (Mar 10) A. Venkataraman et al (2009). Exploring Functional Connectivity in FMRI via Clustering. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp 441-444.
• (Mar 17) David Williams et al (2005). Incomplete Data Classification using Logistic Regression. In Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.
• (Mar 24) Uwe Dick, Peter Haider and Tobias Scheffer (2008). Learning from Incomplete Data with Infinite Imputations. In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.
• (Mar 31) Zhengdong Lu and Miguel Carreira-Perpinan (2008). Constrained Spectral Clustering through Affinity Propagation. In Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.
• (Apr 7) A Smola et al (2005). Kernel Methods for Missing Variables. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, Barbados.
• (Apr 21) 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.
• (Apr 28) Andrew Ng and Michael Jordan (2000). PEGASUS: A policy search method for large MDPs and POMDPs. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp 406--415, Stanford, CA, USA.
• (May 5) Malik Magdon-Ismail and Konstantin Mertsalov (2010). A Permutation Approach to Validation. In Proceedings of the 2010 SIAM International Conference on Data Mining, Columbus, OH, USA.
• (Aug 4) Yoav Freund and Robert Schapire (1999). Large margin classification using the perceptron algorithm. Machine Learning, 37(3):277--296.
• (Aug 18) Thomas G. Dietterich et al (2008). Gradient Tree Boosting for Training Conditional Random Fields. Journal of Machine Learning Research, vol 9, pp 2113-2139.
• (Aug 25) Gal Chechik et al (2007). Max-margin classification of incomplete data. In the 21st Advances in Neural Information Processing Systems (NIPS), Vancouver BC, Canada.
• (Sep 1) David Badre, Andrew Kayser and Mark D'Esposito (2010). Frontal Cortex and the Discovery of Abstract Action Rules. Neuron, vol 66 no. 2, pp 315--326.
• (Sep 8) Richard Dearden, Nir Friedman and Stuart Russell (1998). Bayesian Q-Learning. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), Madison, WI, USA.
• (Sep 15) Carlos Guestrin, Michail Lagoudakis and Ronald Parr (2002). Coordinated Reinforcement Learning. In Proceedings of the 19th International Conference on Machine Learning, Sydney, Australia.
• (Sep 29) Trevor Hastie et al (2004). The entire regularization path for the support vector machine. Journal of Machine Learning Research, vol 5, pp 1391-1415.
• (Oct 6) Donglin Niu et al (2010). Multiple Non-Redundant Spectral Clustering Views. In Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel.
• (Oct 13) Jelle R. Kok et al (2005). Utile Coordination: Learning interdependencies among cooperative agents. Proceedings of the IEEE Symposium on Computational Intelligence and Games.
• (Oct 20) Richard Dearden et al (1999). Model based Bayesian Exploration. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp150-159.
• (Oct 27) Levente Kocsis and Csaba Szepesvari (2006). Bandit-based Monte-Carlo Planning. Proceedings of the 17th European Conference on Machine Learning and the 10th European Conference on Principles and Practice of Knowledge Discovery, pp 282--293, Berlin, Germany.
• (Nov 3) Pedro Domingos et al. Markov Logic Networks: An Interface layer to AI. Synthesis lectures on AI.
• (Nov 17) R. Bunescu and R. Mooney (2007). Multiple Instance Learning for Sparse Positive Bags. In Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR, USA.
• (Dec 01) Wilson, A. and Fern, A. and Tadepalli, P.(2010). Bayesian Policy Search for Multi-Agent Role Discovery. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10).
• (Dec 01) Wilson, A. and Fern, A. and Ray, S. and Tadepalli, P.(2008). Learning and Transferring Roles in Multi-Agent Reinforcement. Transfer Learning for Complex Tasks Workshop, 23rd AAAI Conference on Artificial Intelligence, Chicago, USA.
• (Dec 08) Ando, R.K. and Zhang, T. (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. The Journal of Machine Learning Research.6:1817-1853
• (Dec 22) Vogel, A.C. and Power, J.D. and Petersen, S.E. and Schlaggar, B.L.(2010). Development of the Brain's Functional Network Architecture.Neuropsychology review. pp 1-14.
• (Dec 22) Dosenbach, N.U.F. and Nardos, B. and Cohen, A.L. and Fair, D.A. and Power, J.D. and Church, J.A. and Nelson, S.M. and Wig, G.S. and Vogel, A.C. and Lessov-Schlaggar, C.N. and others (2010). Prediction of Individual Brain Maturity Using fMRI. AAAS. 329:1358-.
• (Jan 05) Sugandh, N. and Ontanon, S. and Ram, A. (2008). On-Line Case-Based Plan Adaptation for Real-Time Strategy Games. 23rd AAAI Conference on Artificial Intelligence. pp 702-707
• (Jan 12) Brafman, R.I. and Tennenholtz, M. (2003). R-max: A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning.JMLR. 3:213--231
• (Jan 19) Tao, Q. and Scott, S.D. and Vinodchandran, NV and Osugi, T.T. and Mueller, B. (2007). Kernels for Generalized Multiple-Instance Learning.IEEE transactions on pattern analysis and machine intelligence.pp 2084--2098
• (Jan 26) Newman, M.E.J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences. 103:8577
• (Jan 26) Danial Lashkari and Ramesh Sridharan and Polina Golland (2010). Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. Advances in Neural Information Processing Systems 23. pp 1252--1260
• (Feb 09) Wunder, M. and Littman, M. and Babes, M.(2010). Classes of Multiagent Q-learning Dynamics with e-greedy Exploration. Proceedings of the 27th International Confer- ence on Machine Learning, Haifa, Israel.
• (Feb 16) Jong, N.K. and Stone, P. (2008). Hierarchical Model-Based Reinforcement Learning - R-MAX + MAXQ.Proceedings of the 25th international conference on Machine learning. pp 432--439
• (Feb 23) Kearns, M. and Mansour, Y. and Ng, A.Y. (2002). A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. Machine Learning. 49:193--208
• (Mar 02) Taylor, M.E. and Jain, M. and Jin, Y. and Yooko, M. and Tambe, M. (2010). When should there be a Me in Team?: distributed multi-agent optimization under uncertainty.Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1-Volume 1. pp 109--116
• (Mar 9) Sridhar Mahadevan (2008). Representation Discovery Using Harmonic Analysis. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan Claypool.
• (Jun 24 2015) R. Jonschkowski and O Brock (2014). State Representation Learning in Robotics. RSS.