A. Weller and T. Jebara. "Bethe Bounds and Approximating the Global Optimum" . arXiv:1301.0015 and CUCS Tech Report 022-12, December 2012.
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T. Jebara and A. Choromanska. "Majorization for CRFs and Latent Likelihoods" . Neural Information Processing Systems (NIPS), December 2012.
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T. Jebara. "Perfect Graphs and Graphical Modeling" . To Appear in Tractability: Practical Approaches to Hard Problems, Edited by Lucas Bordeaux, Youssef Hamadi, Pushmeet Kohli, and Robert Mateescu, Cambridge University Press, 2012.
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Y. Song. "A Behavior-based Approach Towards Statistics-Preserving Network Trace Anonymization" . PhD Thesis, Columbia University, 2012.
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B. Shaw, B. Huang and T. Jebara. "Learning a Distance Metric from a Network" . Neural Information Processing Systems (NIPS), December 2011.
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P. Shivaswamy and T. Jebara. "Variance Penalizing AdaBoost" . Neural Information Processing Systems (NIPS), December 2011.
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B. Huang, B. Shaw and T. Jebara. "Learning a Degree-Augmented Distance Metric From a Network" . Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity Workshop, Neural Information Processing Systems (NIPS), December 2011.
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Y. Song, S. Stolfo and T. Jebara. "Behavior-Based Network Traffic Synthesis" . IEEE International Conference on Technologies for Homeland Security (IEEE HST), November 2011.
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B. Kapicioglu, D. Rosenberg, R. Schapire, and T. Jebara. "Place Recommendation with Implicit Spatial Feedback" . New York Academy of Sciences, Machine Learning Symposium, October 2011.
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A. Moghadam, T. Jebara and H. Schulzrinne. "A Markov Routing Algorithm for Mobile DTNs based on Spatio-Temporal Modeling of Human Movement Data" . Fourteenth ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), 2011.
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B. Huang. "Learning with Degree-Based Subgraph Estimation" . PhD Thesis, Columbia University, 2011.
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B. Shaw. "Graph Embedding and Nonlinear Dimensionality Reduction" . PhD Thesis, Columbia University, 2011.
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B. Huang and T. Jebara. "Fast b-matching via Sufficient Selection Belief Propagation" . Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATs, April 2011.
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T. Jebara. "Multitask Sparsity via Maximum Entropy Discrimination" . Journal of Machine Learning Research (JMLR), 12(Jan):75-110, 2011.
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P. Shivaswamy and T. Jebara. "Laplacian Spectrum Learning" . European Conference on Machine Learning (ECML), 2010.
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P. Shivaswamy and T. Jebara. "Empirical Bernstein Boosting" . Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATs, May 2010.
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B. Huang and T. Jebara. "Collaborative Filtering via Rating Concentration" . Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATs, May 2010.
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T. Jebara. "Graphical Modeling and Inference with Perfect Graphs" . The Learning Workshop, April 2010.
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P. Shivaswamy and T. Jebara. "Maximum Relative Margin and Data-Dependent Regularization" . Journal of Machine Learning Research (JMLR), 11(Feb):747-788, 2010.
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P. Shivaswamy. "Large Relative Margin and Applications" . PhD Thesis, Columbia University, 2010.
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T. Jebara. "MAP Estimation, Message Passing, and Perfect Graphs" . Uncertainty in Artificial Intelligence, UAI, June 2009. Update: the runtime of GroLovSch's method was corrected.
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B. Shaw and T. Jebara. "Structure Preserving Embedding" . International Conference on Machine Learning, ICML, June 2009. BEST PAPER AWARD
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T. Jebara, J. Wang and S.F. Chang. "Graph Construction and b-Matching for Semi-Supervised Learning" . International Conference on Machine Learning, ICML, June 2009.
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B. Huang and T. Jebara. "Exact Graph Structure Estimation with Degree Priors" . International Conference on Machine Learning and Applications, ICMLA, December 2009.
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P. Shivaswamy and T. Jebara. "Structured Prediction with Relative Margin" . International Conference on Machine Learning and Applications, ICMLA, December 2009.
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A. Howard and T. Jebara. "Transformation Learning Via Kernel Alignment" . International Conference on Machine Learning and Applications, ICMLA, December 2009.
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A. Weller, D. Ellis and T. Jebara. "Structured Prediction Models for Chord Transcription of Music Audio" . International Conference on Machine Learning and Applications, ICMLA, December 2009.
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D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, and M. Van Alstyne. "Computational Social Science" . Science, February 6 2009.
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A. Howard and T. Jebara. "Large Margin Transformation Learning" . To appear in Journal of Machine Learning Research (JMLR), 2009. Preliminary version awaiting corrections for photoready.
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C. Lima, U. Lall, T. Jebara, and A.G. Barnston. "Statistical Prediction of ENSO from Subsurface Sea Temperature Using a Nonlinear Dimensionality Reduction" . Journal of Climate, Volume 22, Number 17, Pages 4501-4519, September 1, 2009.
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B. Huang and T. Jebara. "Approximating the Permanent with Belief Propagation" . Technical report on the arXiv, August 12, 2009.
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B. Shaw and T. Jebara. "Dimensionality Reduction, Clustering, and PlaceRank Applied to Spatiotemporal Flow Data" . New York Academy of Sciences - Machine Learning Symposium, November, 2009.
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M. Loecher and T. Jebara. "CitySense: Multiscale Space Time Clustering of GPS Points and Trajectories" . Proceedings of the Joint Statistical Meeting (JSM), August, 2009.
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A. Howard. "Large Margin Transformation Learning" . PhD Thesis, Columbia University, 2009.
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P. Shivaswamy and T. Jebara. "Relative Margin Machines" . Neural Information Processing Systems 21, NIPS, December 2008.
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B. Huang and T. Jebara. "Maximum Likelihood Graph Structure Estimation with Degree Distributions" . Analyzing Graphs: Theory and Applications, NIPS Workshop. December 2008.
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B. Shaw and T. Jebara. "Visualizing Graphs with Structure Preserving Embedding" . Analyzing Graphs: Theory and Applications, NIPS Workshop. December 2008
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W. Jiang, S.F. Chang, T. Jebara and A.C. Loui. "Semantic Concept Classification by Joint Semi-Supervised Learning of Feature Subspaces and Support Vector Machiness" . European Conference on Computer Vision, ECCV, October 2008.
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T. Jebara. "Bayesian Out-Trees" . Uncertainty in Artificial Intelligence, UAI, July 2008.
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T. Jebara. "Out-Tree Dependent Nonparametric Bayesian Inference" . Workshop on Nonparameteric Bayes, July 2008.
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J. Wang, T. Jebara and S.F. Chang. "Graph Transduction via Alternating Minimization" . International Conference on Machine Learning, ICML, July 2008.
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T. Jebara. "Learning from Out-Tree Dependent Data" . Snowbird Machine Learning Workshop, April 2008.
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R. Kondor. "Group theoretical methods in machine learning" . PhD Thesis, Columbia University, May 2008.
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T. Jebara, Y. Song and K. Thadani. "Density Estimation under Independent Similarly Distributed Sampling Assumptions" . Neural Information Processing Systems, NIPS, December 2007.
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A. Howard and T. Jebara. "Learning Monotonic Transformations for Classification" . Neural Information Processing Systems, NIPS, December 2007.
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S. Andrews and T. Jebara. "Graph reconstruction with degree-constrained subgraphs" . Workshop on Statistical Network Models, NIPS, December 2007.
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B. Shaw and T. Jebara. "Minimum Volume Embedding" . Artificial Intelligence and Statistics, AISTATS, March 2007.
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B. Huang and T. Jebara. "Loopy Belief Propagation for Bipartite Maximum Weight b-Matching" . Artificial Intelligence and Statistics, AISTATS, March 2007.
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P. Shivaswamy and T. Jebara. "Ellipsoidal Kernel Machines" . Artificial Intelligence and Statistics, AISTATS, March 2007.
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Addendum on computing the kernelized Minimum Volume Ellipsoid
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R. Kondor, A. Howard and T. Jebara. "Multi-Object Tracking with Representations of the Symmetric Group" . Artificial Intelligence and Statistics, AISTATS, March 2007.
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T. Jebara, Y. Song and K. Thadani. "Spectral Clustering and Embedding with Hidden Markov Models" . European Conference on Machine Learning, ECML, September 2007.
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T. Jebara, B. Shaw and A. Howard. "Optimizing Eigengaps and Spectral Functions using Iterated SDP" . Learning Workshop, 2007.
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R. Kondor and T. Jebara. "Gaussian and Wishart Hyperkernels" . In Neural Information Processing Systems (NIPS), December 2006.
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M. Mandel, D. Ellis and T. Jebara. "An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments" . In Neural Information Processing Systems (NIPS), December 2006.
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T. Jebara and V. Shchogolev. "B-Matching for Spectral Clustering" . European Conference on Machine Learning, ECML, September 2006.
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D. Lewis, T. Jebara and W.S. Noble. "Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure" . Bioinformatics. 22(22):2753-2760, 2006.
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P. Shivaswamy and T. Jebara. "Permutation Invariant SVMs" . International Conference on Machine Learning, ICML, June 2006.
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D. Lewis, T. Jebara and W. Noble. "Non-Stationary Kernel Combination" . International Conference on Machine Learning, ICML, June 2006.
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D. Lewis. "Combining Kernels for Classification" . PhD Thesis, Columbia University, May 2006.
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T. Jebara, B. Shaw and V. Shchogolev. "B-Matching for Embedding" . Snowbird Machine Learning Conference, April 2006.
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I. R. Kondor, G. Csanyi, S.E. Ahnert and T. Jebara. "Multi Facet Learning in Hilbert Spaces" . Columbia University, Computer Science Technical Report, CUCS-054-05. 2005.
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T. Jebara and P. Long. "Tree Dependent Identically Distributed Learning" . Columbia University, Computer Science Technical Report, CUCS-050-05. 2005.
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A. Howard and T. Jebara. "Square Root Propagation" . Columbia University, Computer Science Technical Report, CUCS-040-05. 2005.
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K. Nishino, S.K. Nayar and T. Jebara. "Clustered Blockwise PCA for Representing Visual Data" . IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 10, p. 1675, October 2005.
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T. Jebara, R. Kondor and A. Howard. "Probability Product Kernels" . Journal of Machine Learning Research (JMLR), Special Topic on Learning Theory, 5(Jul):819-844, 2004.
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A. Howard and T. Jebara. "Dynamical Systems Strees" . Uncertainty in Artificial Intelligence, UAI, July 2004.
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T. Jebara. "Kernelizing Sorting, Permutation and Alignment for Minimum Volume PCA" . Conference on Learning Theory, COLT, July 2004.
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T. Jebara. "Multi-Task Feature and Kernel Selection for SVMs" . International Conference on Machine Learning, ICML, July 2004.
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R. Pelossof, A. Miller, P. Allen and T. Jebara. "An SVM Learning Approach to Robotic Grasping" . Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), May 2004.
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T. Jebara, and Y. Bengio. "Orbit Learning using Convex Optimization" . Snowbird Machine Learning, April 2004.
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R. Kondor, T. Jebara, G. Csanyi, S. Ahnert. "Learning from Derivatives and other Linear Functionals" . Snowbird Machine Learning, April 2004.
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T. Jebara. Machine Learning: Discriminative and Generative . Kluwer. ISBN 1-4020-7647-9.
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T. Jebara. "Images as Bags of Pixels" . International Conference on Computer Vision, ICCV 2003.
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T. Jebara and R. Kondor. "Bhattacharyya and Expected Likelihood Kernels" . Conference on Learning Theory, COLT/KW 2003.
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A. Smola and R. Kondor. "Kernels and Regularization on Graphs" . Conference on Learning Theory, COLT/KW 2003.
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R. Kondor and T. Jebara. "A Kernel Between Sets of Vectors" . International Conference on Machine Learning, ICML 2003.
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T. Jebara and A. Howard. "Dynamical Systems Trees" . Columbia University, Computer Science Technical Report, CUCS-028-03.
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T. Jebara. "Convex Invariance Learning" . Artificial Intelligence and Statistics, AISTAT 2003. (Longer Version)
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T. Jebara and A. Pentland. "Statistical Imitative Learning from Perceptual Data" . 2nd International Conference on Development and Learning, ICDL'02, June 2002.
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T. Jebara and T. Jaakkola. "Multi-Task SVM Feature Selection" . Snowbird Machine Learning Workshop, 2002.
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A. Kundaje, O. Antar, T. Jebara and C. Leslie. "Learning Regulatory Networks from Sparsely Sampled Time Series Expression Data" . Technical Report, 2002.
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R.I. Kondor and J. Lafferty. "Diffusion Kernels on Graphs and Other Discrete Input Spaces" . International Conference on Machine Learning (ICML), 2002.
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A. Honig, A. Howard, E. Eskin, and S. Stolfo. " An Architecture for the Deployment of Data Mining-based Intrusion Detection Systems." . Applications of Data Mining in Computer Security. Kluwer 2002.
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T. Jebara. "Discriminative, Generative and Imitative Learning" . PhD Thesis, Media Laboratory, MIT, December 2001.
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B. Schiele, T. Jebara, and N. Oliver. "Sensory Augmented Computing: Wearing the Museum's Guide" . IEEE Micro 21 (3), May 2001.
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T. Jebara, and A. Pentland. "On Reversing Jensen's Inequality" . In Neural Information Processing Systems 13, NIPS 13. December 2000.
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B. Moghaddam, T. Jebara, and A. Pentland. "Bayesian Face Recognition" . Pattern Recognition, Vol 33, Issue 11, pps 1771-1782. November 2000.
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T. Jebara, Y. Ivanov, A. Rahimi and A. Pentland. "Tracking Conversational Context for Machine Mediation of Human Discourse" . In AAAI Fall 2000 Symposium - Socially Intelligent Agents - The Human in the Loop. Nov. 2000.
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A. Pentland, T. Jebara, B. Clarkson and S. Basu. "Learning Techniques in Audiovisual Information Processing" . In 15th International Conference on Pattern Recognition, ICPR 15 TUTORIAL. September 2000.
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T. Jebara and T. Jaakkola. "Feature Selection and Dualities in Maximum Entropy Discrimination" . In 16th Conference on Uncertainty in Artificial Intelligence, UAI 2000. July 2000.
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T. Jaakkola, M. Meila and T. Jebara. "Maximum Entropy Discrimination" . In Neural Information Processing Systems 12, NIPS 12. December 1999.
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J. Strom, T. Jebara, S. Basu, and A. Pentland. "Real Time Tracking and Modeling of Faces: An EKF-based Analysis by Synthesis Approach" . Proceedings of the Modelling People Workshop at ICCV'99, August 1999.
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T. Jebara, A. Azarbayejani, and A. Pentland. "3D Structure from 2D Motion" . In IEEE Signal Processing Magazine, "3D And Stereoscopic Visual Communication" May 1999, Vol. 16. No. 3.
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T. Jebara and A. Pentland. "Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour". In International Conference on Vision Systems, ICVS, January, 1999.
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B. Schiele, N. Oliver, T. Jebara and A. Pentland. "An Interactive Computer Vision System DyPERS: Dynamic Personal Enhanced Reality System". In International Conference on Vision Systems, ICVS, January, 1999.
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T. Choudhury, B. Clarkson, T. Jebara and A. Pentland. "Multimodal Person Recognition using Unconstrained Audio and Video". International Conference on Audio and Video-Based Biometric Person Authentication, AVBPA, 1999.
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T. Jebara and A. Pentland. "Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm". In Neural Information Processing Systems 11, NIPS, Dec. 1998.
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B. Moghaddam, T. Jebara and A. Pentland. "Bayesian Modeling of Facial Similarity". In Neural Information Processing Systems 11, NIPS, Dec. 1998.
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T. Jebara, B. Schiele, N. Oliver and A. Pentland. "DyPERS: Dynamic Personal Enhanced Reality System". In Proceedings of the 1998 Image Understanding Workshop, November 1998.
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T. Jebara. "Action Reaction Learning: Analysis and Synthesis of Human Behaviour" . Master's Thesis, Media Laboratory, MIT, May 1998.
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T. Jebara, K. Russell and A. Pentland. "Mixtures of Eigenfeatures for Real-Time Structure from Texture" . International Conference on Computer Vision (ICCV), January 1998.
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T. Jebara and A. Pentland. "Action Reaction Learning: Analysis and Synthesis of Human Behaviour". In Workshop on the Interpretation of Visual Motion at the Conference on Computer Vision and Pattern Recognition, CVPR, June 1998.
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T. Starner, B. Schiele, B. Rhodes, T. Jebara, N. Oliver, J. Weaver and A. Pentland. "Augmented Realities Integrating User and Physical Models". In Workshop on Augmented Reality, 1998.
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T. Jebara, C. Eyster, J. Weaver, T. Starner and A. Pentland. "Stochasticks: Augmenting the Billiards Experience with Probabilistic Vision and Wearable Computers". International Symposium on Wearable Computers (ISWC), Oct. 1997.
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T. Jebara and A. Pentland. "Parametrized Structure from Motion for 3D Adaptive Feedback Tracking of Faces". In IEEE Conference on Computer Vision and Pattern Recognition, CVPR, June 1997.
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T. Jebara. "3D Pose Estimation and Normalization for Face Recognition" . Undergraduate Thesis, Center for Intelligent Machines, McGill University, May 1996.
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