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Selected Papers


Most of these works have copyright protection and cannot be redistributed without permission. This work was supported in part by the following grants from the National Science Foundation: III-1526914, IIS-1451500, CCF-1302269, IIS-1117631, IIS-0347499 and CCR-0312690.

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2020
  • H. Steck, M. Dimakopoulou, N. Riabov and T. Jebara. "ADMM SLIM: Sparse recommendations for many users." The 13th ACM International WSDM Conference (WSDM), 2020.
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  • 2019
  • A. Stirn, T. Jebara and D. Knowles. "A new distribution on the simplex with auto-encoding applications." Neural Information Processing Systems (NeurIPS), arXiv:1905.12052, 2019.
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  • E. Elahi, W. Wang, D. Ray, A. Fenton and T. Jebara. "Variational low rank multinomials for collaborative filtering with side-information." The ACM Conference Series on Recommender Systems (RecSys), 2019.
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  • D. Tang, D. Liang, T. Jebara and N. Ruozzi. "Correlated variational auto-encoders." International Conference on Machine Learning (ICML), 2019.
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  • N. Vlassis, A. Bibaut, M. Dimakopoulou and T. Jebara. "On the design of estimators for bandit off-policy evaluation." International Conference on Machine Learning (ICML), 2019.
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  • J. Xu, D. Tang and T. Jebara. "Active multitask learning with committees." Adaptive and Multi-Task Learning Workshop at the International Conference on Machine Learning (ICML), 2019.
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  • Y. Yan, T. Jebara, R. Abernathy, J. Goes and H. Gomes. "Robust learning algorithms for capturing oceanic dynamics and transport of noctiluca blooms using linear dynamical models." PLOS ONE 14(6): e0218183, June 2019.
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  • D. Hubbard, B. Rostykus, Y. Raimond, and T. Jebara. "Beta survival models." arXiv:1905.03818, 2019.
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  • D. Tang, D. Liang, N. Ruozzi and T. Jebara. "Learning correlated latent representations with adaptive priors." arXiv:1906.06419, 2019.
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  • M. Dimakopoulou, N. Vlassis, and T. Jebara. "Marginal posterior sampling for slate bandits." International Joint Conference on Artificial Intelligence (IJCAI), 2019.
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  • 2018
  • D. Tang, X. Li, J. Gao, C. Wang, L. Li and T. Jebara. "Subgoal discovery for hierarchical dialogue policy learning." Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
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  • A. Aravkin, A. Choromanska, L. Deng, G. Heigold, T. Jebara, D. Kanevsky and S. Wright. Log-Linear Models, Extensions, and Applications. MIT Press, 2018.
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  • T. Jebara. "A refinement of Bennett's inequality with applications to portfolio optimization." arXiv:1804.05454, 2018.
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  • A. Stirn and T. Jebara. "Thompson sampling for noncompliant bandits." arXiv:1812.00856, 2018.
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  • G. Karamanolakis, K. Cherian, A. Narayan, J. Yuan, D. Tang, and T. Jebara. "Item recommendation with variational autoencoders and heterogenous priors." 3rd Workshop on Deep Learning for Recommender Systems (DLRS 2018), arXiv:1807.06651, 2018.
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  • D. Liang, R. Krishnan, M. Hoffman and T. Jebara. "Variational autoencoders for collaborative filtering." International World Wide Web Conference (WWW), 2018.
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  • 2017
  • A. Chandrashekar, F. Amat, J. Basilico and T. Jebara. "Artwork personalization at Netflix." Medium Netflix Technology Blog, 2017.
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  • L. Carrillo-Reid, S. Han, E. Taralova, T. Jebara, and R. Yuste. "Identification and targeting of cortical ensembles." bioRxiv 226514, 2017.
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  • S. Zimmeck, J. Li, H. Kim, S. Bellovin and T. Jebara. "A Privacy analysis of cross-device tracking." 26th USENIX Security Symposium (USENIX), 2017.
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  • D. Tang and T. Jebara. "Initialization and coordinate optimization for multi-way matching." Artificial Intelligence and Statistics (AISTATs), 2017.
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  • G. Gidel, T. Jebara and S. Lacoste-Julien. "Frank-Wolfe algorithms for saddle point problems." Artificial Intelligence and Statistics (AISTATs), 2017.
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  • 2016
  • A. Choromanska, K. Choromanski, M. Bojarski, T. Jebara, S. Kumar and Y. LeCun. "Binary embeddings with structured hashed projections." International Conference on Machine Learning (ICML), 2016.
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  • F.-H. Su, J. Bell, K. Harvey, S. Sethumadhavan, G. Kaiser and T. Jebara. "Code relatives: Detecting similarly behaving software." International Symposium on the Foundations of Software Enginerring (FSE), 2016.
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  • K. Tang, N. Ruozzi, D. Belanger, and T. Jebara. "Bethe learning of graphical models via MAP decoding." International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
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  • 2015
  • B. Kapicioglu, D. Rosenberg, R. Schapire and T. Jebara, "Collaborative place models." International Joint Conferences on Artificial Intelligence (IJCAI), 2015.
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  • K. Choromanski and T. Jebara. "Coloring tournaments with forbidden substructures" Technical report on the arXiv, April, 2015.
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  • K. Tang, N. Ruozzi, D. Belanger and T. Jebara. "Bethe learning of conditional random fields via MAP decoding" Technical report on the arXiv, March, 2015.
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  • K.Tang, H. Gubert, R. Tonge, A. Wang, L. Wu, D. Campbell, C. Kedzie, L. Wang, A. Russell, A. Kimball, A. Kambadur, G. Mann, S. Pacifico, J. Hodson, D. Yao, K. McKeown, T. Jebara, "Learning a graphical model of Bloomberg financial and news data." Data, Algorithms and Problems on Graphs (DAPG) Workshop, 2015.
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  • E. Taralova, T. Jebara, R.Yuste, "Functional models of mouse visual cortex." Data, Algorithms and Problems on Graphs (DAPG) Workshop, 2015.
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  • 2014
  • A. Weller and T. Jebara, "Clamping variables and approximate inference." Neural Information Processing Systems (NIPS), 2014.
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  • N. Ruozzi and T. Jebara, "Making pairwise binary graphical models attractive." Neural Information Processing Systems (NIPS), 2014.
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  • A. Weller and T. Jebara, "Approximating the Bethe partition function." Uncertainty in Artificial Intelligence (UAI), 2014.
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  • A. Weller, K. Tang, D. Sontag and T. Jebara, "Understanding the Bethe approximation: When and how can it go wrong?" Uncertainty in Artificial Intelligence (UAI), 2014.
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  • S.M. Bellovin, R.M. Hutchins, T. Jebara and S. Zimmeck, "When enough is enough: Location tracking, mosaic theory and machine learning." 8 New York University Journal of Law & Liberty 556, 2014.
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  • A. Aravkin, A. Choromanska, T. Jebara, and D. Kanevsky. "Semistochastic quadratic bound methods." Second International Conference on Learning Representations, (ICLR), Workshop Proceedings, 2014.
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  • B. Kapicioglu, D. Rosenberg, R. Schapire, and T. Jebara. "Collaborative ranking for local preferences." Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), April 2014.
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  • F. Xu, K. Choromanski, S. Kumar, T. Jebara and S.-F. Chang. "On learning from label proportions" Technical report on the arXiv, February, 2014.
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  • T. Jebara. "Perfect graphs and graphical modeling" In Tractability: Practical Approaches to Hard Problems, Edited by Lucas Bordeaux, Youssef Hamadi, Pushmeet Kohli, and Robert Mateescu, Cambridge University Press, 2014.
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  • 2013
  • K. Choromanski, T. Jebara and K. Tang. "Adaptive anonymity via b-matching" Neural Information Processing Systems (NIPS), December 2013.
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  • J. Merel, R. Fox, T. Jebara, and L. Paninski. "A multi-agent control framework for co-adaptation in brain-computer interfaces" Neural Information Processing Systems (NIPS), December 2013.
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  • K. Tang, A. Weller and T. Jebara. "Network ranking with Bethe pseudomarginals" Neural Information Processing Systems (NIPS), Workshop on Discrete Optimization in Machine Learning, December 2013.
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  • A. Choromanska, H. Kim, T. Jebara, M. Mohan and C. Monteleoni. "Fast spectral clustering via the Nystrom method" Algorithmic Learning Theory (ALT), October 2013.
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  • A. Weller and T. Jebara. "On MAP inference by MWSS on perfect graphs" Uncertainty in Artificial Intelligence (UAI), July 2013.
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  • F.X. Yu, D. Liu, S. Kumar, T. Jebara, and S.F. Chang. " SVM for learning with label proportions" International Conference on Machine Learning (ICML), June 2013.
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  • S. Bellovin, R. Hutchins, T. Jebara and S. Zimmeck. "When enough is enough: Location tracking, mosaic theory and machine learning" Privacy Law Scholars Conference (PLSC), June 2013.
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  • J. Wang, T. Jebara and S.F. Chang. "Semi-supervised learning using greedy max-cut" Journal of Machine Learning Research (JMLR), 14(Mar):771-800, 2013.
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  • A. Weller and T. Jebara. "Bethe bounds and approximating the global optimum" Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATs), April 2013.
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  • 2012
  • T. Jebara and A. Choromanska. "Majorization for CRFs and latent likelihoods" Neural Information Processing Systems (NIPS), December 2012.
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  • A. Weller and T. Jebara. "Bethe bounds and approximating the global optimum" arXiv:1301.0015 and CUCS Tech Report 022-12 and 2013 Information Theory and Applications Workshop (ITA), December 2012.
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  • 2011
  • 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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  • Y. Song, S. Stolfo and T. Jebara. "Markov models for network-behavior modeling and anonymization" Columbia University, Computer Science Technical Report, CUCS-029-11, 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 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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  • 2010
  • 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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  • 2009
  • 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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  • 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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  • 2008
  • 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 machines" 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" The Learning Workshop, April 2008.
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  • 2007
  • T. Jebara, Y. Song and K. Thadani. "Density estimation under independent similarly distributed sampling assumptions" Neural Information Processing Systems 20 (NIPS), December 2007.
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  • A. Howard and T. Jebara. "Learning monotonic transformations for classification" Neural Information Processing Systems 20 (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. Huang and T. Jebara. "Approximating the permanent with belief propagation" New York Academy of Sciences - Machine Learning Symposium, 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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  • 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" The Learning Workshop, 2007.
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  • 2006
  • R. Kondor and T. Jebara. "Gaussian and Wishart hyperkernels" Neural Information Processing Systems 19 (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 19 (NIPS), December 2006.
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  • S. Andrews and T. Jebara. "Structured network learning" Workshop on Learning to Compare Examples (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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  • T. Jebara, B. Shaw and V. Shchogolev. "B-matching for embedding" The Learning Workshop, April 2006.
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  • 2005
  • 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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  • 2004
  • 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 trees" 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" The Learning Workshop, April 2004.
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  • R. Kondor, T. Jebara, G. Csanyi, S. Ahnert. "Learning from derivatives and other linear functionals." The Learning Workshop, April 2004.
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  • J. Triesch and T. Jebara, Editors. Proceedings of the 2004 International Conference on Development and Learning (ICDL), October 2004.
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  • 2003
  • T. Jebara. Machine learning: Discriminative and generative Kluwer, 2003. 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), 2003.
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  • R. Kondor and T. Jebara. "A kernel between sets of vectors" International Conference on Machine Learning (ICML), 2003. BEST STUDENT PAPER AWARD
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  • T. Jebara. "Convex invariance learning" Artificial Intelligence and Statistics (AISTATs), 2003. (Longer Version)
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  • 2002
  • T. Jebara and A. Pentland. "Statistical imitative learning from perceptual data" 2nd International Conference on Development and Learning (ICDL), June 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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  • 2001
  • 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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  • 2000
  • T. Jebara, and A. Pentland. "On reversing Jensen's inequality" In Neural Information Processing Systems 13 (NIPS), December 2000.
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  • B. Moghaddam, T. Jebara, and A. Pentland. "Bayesian face recognition" Pattern Recognition, Vol 33:11, pps 1771-1782. November 2000. HONORABLE MENTION AWARD
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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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  • T. Jebara and T. Jaakkola. "Feature selection and dualities in maximum entropy discrimination" In 16th Conference on Uncertainty in Artificial Intelligence (UAI), July 2000.
    Note the typo: E_P{\theta_i,s_i} = Logistic[ 0.5 W_i^2 + \log \frac{p_0}{1-p_0} ] W_i.
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  • 1999
  • T. Jaakkola, M. Meila and T. Jebara. "Maximum entropy discrimination" In Neural Information Processing Systems 12 (NIPS), 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, 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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  • 1998
  • T. Jebara and A. Pentland. "Maximum conditional likelihood via bound maximization and the CEM algorithm" Neural Information Processing Systems 11 (NIPS), Dec. 1998.
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  • B. Moghaddam, T. Jebara and A. Pentland. "Bayesian modeling of facial similarity" Neural Information Processing Systems 11 (NIPS), December 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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  • 1997
  • 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." Computer Vision and Pattern Recognition (CVPR), June 1997.
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  • 1996
  • T. Jebara. 3D pose estimation and normalization for face recognition Undergraduate Thesis, Center for Intelligent Machines, McGill University, May 1996.
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