M. I. Jordan and T. J. Sejnowski, Eds., Graphical Models: Foundations of Neural Computation, Computational Neuroscience, Cambridge, MA: MIT Press, 2001.
M. I. Jordan, Ed., Learning in Graphical Models, Adaptive Computation and Machine Learning, Cambridge, MA: MIT Press, 1999.
Book chapters or sections
S. Sankararaman, G. Kimmel, E. Halperin, and M. Jordan, "On the inference of ancestries in admixed populations," in Research in Computational Molecular Biology: Proc. 12th Annual Intl. Conf. (RECOMB 2008), M. Vingron and L. Wong, Eds., Lecture Notes in Computer Science::Lecture Notes in Bioinformatics, Vol. 4955, Berlin, Germany: Springer-Verlag, 2008, pp. 424-433.
L. Huang, X. Nguyen, M. Garofalakis, M. Jordan, A. D. Joseph, and N. Taft, "In-network PCA and anomaly detection," in Advances in Neural Information Processing Systems 19: Proc. 20th Annual Conf. (NIPS 2006), B. Scholkopf, J. Platt, and T. Hofmann, Eds., Advances in Neural Information Processing Systems, Vol. 19, Cambridge, MA: MIT Press, 2007, pp. 617-624.
M. Wainwright and M. Jordan, "A variational principle for graphical models," in New Directions in Statistical Signal Processing: From Systems to Brain, S. Haykin, J. C. Principe, T. J. Sejnowski, and J. McWhirter, Eds., Neural Information Processing, Cambridge, MA: MIT Press, 2006, pp. 155-202.
N. D. Lawrence and M. Jordan, "Gaussian processes and the null-category noise model," in Semi-Supervised Learning, O. Chapelle, B. Schoelkopf, and A. Zien, Eds., Cambridge, MA: MIT Press, 2006, pp. 138-144.
X. Nguyen, M. Wainwright, and M. Jordan, "Divergences, surrogate loss functions and experimental design," in Advances in Neural Information Processing Systems 18: Proc. 19th Annual Conf. (NIPS 2005), Y. Weiss, B. Scholkopf, and J. Platt, Eds., Neural Information Processing Systems, Vol. 18, Cambridge, MA: MIT Press, 2006, pp. 1011-1018.
B. Taskar, S. Lacoste Julien, and M. Jordan, "Structured prediction via the extragradient method," in Advances in Neural Information Processing Systems 18: Proc. 19th Annual Conf. (NIPS 2005), Y. Weiss, B. Schoelkopf, and J. Platt, Eds., Advances in Neural Information Processing Systems, Vol. 18, Cambridge, MA: MIT Press, 2006, pp. 1345-1352.
P. Flaherty, M. Jordan, and A. P. Arkin, "Robust design of biological experiments," in Advances in Neural Information Processing Systems 18: Proc. 19th Annual Conf. (NIPS 2005), Y. Weiss, B. Schoelkopf, and J. Platt, Eds., Advances in Neural Information Processing Systems, Vol. 18, Cambridge, MA: MIT Press, 2006, pp. 363-370.
Y. W. Teh, M. Jordan, M. J. Beal, and D. M. Blei, "Sharing clusters among related groups: Hierarchical Dirichlet processes," in Advances in Neural Information Processing Systems 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 1385-1392.
F. R. Bach and M. Jordan, "Blind one-microphone speech separation: A spectral learning approch," in Advances in Neural Information Processing 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 65-72.
A. d'Aspremont, L. El Ghaoui, M. Jordan, and G. R. G. Lanckriet, "A direct formulation for sparse PCA using semidefinite programming," in Advances in Neural Information Processing Systems 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 41-48.
N. D. Lawrence and M. Jordan, "Semi-supervised learning via Gaussian processes," in Advances in Neural Information Processing Systems 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 753-760.
F. R. Bach, R. Thibaux, and M. Jordan, "Computing regularization paths for learning multiple kernels," in Advances in Neural Information Processing Systems 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 73-80.
N. D. Lawrence, J. C. Platt, and M. Jordan, "Extensions of the informative vector machine," in Deterministic and Statistical Methods in Machine Learning: Proc. 1st Intl. Sheffield Machine Learning Workshop. Revised Lectures, J. Winkler, M. Niranjan, and N. Lawrence, Eds., Lecture Notes in Computer Science, Vol. 3635, Berlin, Germany: Springer-Verlag, 2005, pp. 56-87.
M. Wainwright and M. Jordan, "Semidefinite relaxations for approximate inference on graphs with cycles," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 369-376.
F. R. Bach and M. Jordan, "Learning spectral clustering," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 305-312.
D. M. Blei, T. L. Griffiths, M. Jordan, and J. B. Tenenbaum, "Hierarchical topic models and the nested Chinese restaurant process," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 17-24.
K. Fukumizu, F. R. Bach, and M. Jordan, "Kernel dimensionality reduction for supervised learning," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 81-88.
P. Bartlett, M. Jordan, and J. D. McAuliffe, "Large margin classifiers: Convex loss, low noise, and convergence rates," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 1173-1180.
X. Nguyen and M. Jordan, "On the concentration of expectation and approximate inference in layered networks," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 393-400.
A. X. Zheng, M. Jordan, B. Liblit, and A. Aiken, "Statistical debugging of sampled programs," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 603-610.
A. Y. Ng, H. J. Kim, M. Jordan, and S. S. Sastry, "Autonomous helicopter flight via reinforcement learning," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 799-806.
F. R. Bach and M. Jordan, "Learning graphical models with Mercer kernels," in Advances in Neural Information Processing Systems: Proc. 16th Annual Conf. (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 1033-1040.
G. R. G. Lanckriet, L. El Ghaoui, and M. Jordan, "Robust novelty detection with single-class MPM," in Advances in Neural Information Processing Systems: Proc. 16th Annual Conf. (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 929-936.
E. P. Xing, A. Y. Ng, M. Jordan, and S. J. Russell, "Distance metric learning with application to clustering with side-information," in Proc. 16th Annual Advances in Neural Information Processing Systems (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Bradford Books, Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 521-528.
E. Todorov and M. Jordan, "A minimal intervention principle for coordinated movement," in Advances in Neural Information Processing Systems: Proc. 16th Annual Conf. (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 27-34.
E. P. Xing, M. Jordan, R. M. Karp, and S. J. Russell, "A hierarchical Bayesian Markovian model for motifs in biopolymer sequences," in Proc. 16th Annual Advances in Neural Information Processing Systems (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Bradford Books, Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 1513-1520.
M. Jordan, "Neural Networks," in The MIT Encyclopedia of Cognitive Sciences (MITECS), R. A. Wilson and F. C. Keil, Eds., Bradford Books, Cambridge, MA: MIT Press, 1999, pp. 597-598.
R. Parr and S. J. Russell, "Reinforcement learning with hierarchies of machines," in Advances in Neural Information Processing Systems 10: Proc. 11th Annual Conf. (NIPS 1997), M. Jordan, M. J. Kearns, and S. A. Solla, Eds., Bradford Books, Vol. 10, Cambridge, MA: MIT Press, 1998, pp. 1043-1049.
B. Shi, S. Du, W. Su, and M. Jordan, "Understanding the acceleration phenomenon via high-resolution differential equations," Mathematical Programming, vol. 5, pp. 634-648, April 2022.
A. Adhikari, J. DeNero, and M. Jordan, "Interleaving computational and inferential thinking: Data science for undergraduates at Berkeley," {Harvard Data Science Review, vol. 2, pp. 1-24, Sep. 2021.
A. El Alaoui, F. Krzakala, and M. Jordan, "Fundamental limits of detection in the spiked Wigner model," Annals of Statistics, vol. 48, pp. 863-885, July 2021.
M. Muehlbach and M. Jordan, "Optimization with momentum: Dynamical, control-theoretic, and symplectic perspectives," Journal of Machine Learning Research, vol. 22, pp. 1-50, July 2021.
W. Mou, M. Yi-An, M. Wainwright, P. Bartlett, and M. Jordan, "High-order Langevin diffusion yields an accelerated MCMC algorithm," Journal of Machine Learning Research, vol. 22, pp. 1-48, March 2021.
X. Dai and M. Jordan, "Learning strategies in decentralized matching markets under uncertain preferences," Journal of Machine Learning Research, vol. 22, pp. 1-50, March 2021.
I. Stoica, D. Song, R. A. Popa, D. A. Patterson, M. W. Mahoney, R. H. Katz, A. D. Joseph, M. Jordan, J. M. Hellerstein, J. Gonzalez, and et al, "A berkeley view of systems challenges for AI," arXiv preprint arXiv:1712.05855, 2017.
A. Talwalkar, J. Liptrap, J. Newcomb, C. Hartl, J. Terhorst, K. Curtis, M. Bresler, Y. S. Song, M. Jordan, and D. A. Patterson, "SMaSH: A benchmarking toolkit for human genome variant calling," Bioinformatics, vol. 30, no. 19, pp. 2787-2795, June 2014.
G. Obozinski, G. Lanckriet, C. Grant, M. Jordan, and W. S. Noble, "Consistent probabilistic outputs for protein function prediction," Genome Biology: Quantitative Inference of Gene Function from Diverse Large-Scale Datasets, vol. 9, no. Suppl 1, pp. S6:1-8, June 2008.
L. Pena-Castillo, M. Tasan, C. L. Myers, H. Lee, T. Joshi, C. Zhang, Y. Guan, M. leone, A. Pagnani, W. K. Kim, C. Krumpelman, W. Tian, G. Obozinski, Y. Qi, S. Mostafavi, G. N. Lin, G. F. Berriz, F. D. Gibbons, G. Lanckriet, J. Qiu, C. Grant, Z. Barutcuoglu, D. P. Hill, D. Warde-Farley, C. Grouios, D. Ray, J. A. Blake, M. Deng, M. Jordan, W. S. Noble, Q. Morris, J. Klein-Seetharaman, Z. Bar-Joseph, T. Chen, F. Sun, O. G. Troyanskaya, E. M. Marcotte, D. Xu, T. R. Hughes, and F. P. roth, "A critical assessment of Mus musculus gene function prediction using integrated genomic evidence," Genome Biology: Quantitative Inference of Gene Function from Diverse Large-Scale Datasets, vol. 9, no. Suppl 1, pp. S2:1-19, June 2008.
Y. W. Teh, M. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical Dirichlet processes," J. American Statistical Association, vol. 101, no. 476, pp. 1566-1581, Dec. 2006.
Y. W. Teh, M. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical Dirichlet processes," Journal of the American Statistical Association, vol. 101, pp. 1566-1581, June 2006.
P. Bartlett, M. Jordan, and J. D. McAuliffe, "Convexity, classification, and risk bounds," J. American Statistical Association, vol. 101, no. 473, pp. 138-156, March 2006.
B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. Jordan, "Scalable statistical bug isolation," ACM SIGPLAN Notices, vol. 40, no. 6, pp. 15-26, June 2005.
B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, and S. S. Sastry, "Kalman filtering with intermittent observations," IEEE Trans. Automatic Control: Special Issue on Sensor Networks, vol. 49, no. 9, pp. 1453-1464, Sep. 2004.
G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. Jordan, "A robust minimax approach to classification," The J. of Machine Learning, vol. 3, pp. 555-582, March 2003.
D. M. Blei, A. Y. Ng, and M. Jordan, "Latent Dirichlet allocation," J. Machine Learning Research, vol. 3, pp. 993-1022, Jan. 2003.
M. Jagadeesan, A. Wei, M. Jordan, and J. Steinhardt, "Learning equilibria in matching markets from bandit feedback," in Advances in Neural Information Processing Systems, 2021.
J. D. Lee, M. Jordan, B. Recht, and M. Simchowitz, "Gradient Descent Only Converges to Minimizers," in Proceedings of the 29th Conference on Learning Theory, {COLT} 2016, New York, USA, June 23-26, 2016, 2016, pp. 1246--1257.
X. Pan, D. Papailiopoulos, S. Omyak, B. Recht, K. Ramchandran, and M. Jordan, "Parallel correlation clustering on big graphs," in Advances in Neural Information Processing Systems 28, 2015, pp. 82--90.
A. Bloniarz, A. Talwalker, J. Terhorst, M. Jordan, D. A. Patterson, B. Yu, and Y. Song, "Changepoint Analysis for Efficient Variant Calling," in Research in Computational Molecular Biology, R. Sharan, Ed., Lecture Notes in Computer Science, Vol. 8394, Springer International Publishing, 2014, pp. 20-34.
W. Xu, L. Huang, A. Fox, D. A. Patterson, and M. Jordan, "Detecting large-scale system problems by mining console logs," in Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles, SOSP, New York, NY: ACM, 2009, pp. 117-132.
P. Liang, D. Klein, and M. Jordan, "Agreement-based learning," in Advances in Neural Information Processing Systems 20: Proc. 21st Annual Conf. (NIPS 2007), J. Platt, D. Koller, Y. Singer, and A. McCallum, Eds., Vol. 20, La Jolla,CA: Neural Information Processing Systems Foundation, 2008, pp. 8 pg.
B. Blum, M. Jordan, D. E. Kim, R. Das, P. Bradley, and D. Baker, "Feature selection methods for improving protein structure prediction with Rosetta," in Advances in Neural Information Processing Systems 20: Proc. 21st Annual Conf. (NIPS 2007), J. Platt, D. Koller, Y. Singer, and A. McCallum, Eds., Vol. 20, La Jolla, CA: Neural Information Processing Systems Foundation, 2008, pp. 8 pg.
C. Sutton and M. Jordan, "Probabilistic inference in queueing networks," in Proc. 3rd Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SysML 2008), Berkeley, CA: USENIX Association, 2008, pp. 6 pg.
W. Xu, L. Huang, A. Fox, D. A. Patterson, and M. Jordan, "Mining console logs for large-scale system problem detection," in Proc. 3rd Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SysML 2008), Berkeley, CA: USENIX Association, 2008, pp. 6 pg.
E. B. Fox, E. B. Sudderth, M. Jordan, and A. S. Willsky, "An HDP-HMM for systems with state persistence," in Proc. 25th Intl. Conf. on Machine Learning (ICML 2008), A. McCallum and S. Roweis, Eds., ACM International Conference Proceeding Series, Vol. 307, New York, NY: The Association for Computing Machinery, Inc., 2008, pp. 312-319.
J. J. Kivinen, E. B. Sudderth, and M. Jordan, "Image denoising with nonparametric hidden Markov trees," in Proc. IEEE Intl. Conf. on Image Processing (ICIP 2007), Vol. 3, Piscataway, NJ: IEEE Press, 2007, pp. 121-124.
P. Liang, M. Jordan, and B. Taskar, "A permutation-augmented sampler for DP mixture models," in Proc. 24th Intl. Conf. on Machine Learning, Z. Ghahramani, Ed., ACM International Conference Proceeding, Vol. 227, New York, NY: The Association for Computing Machinery, Inc., 2007, pp. 545-552.
P. Liang, S. Petrov, M. Jordan, and D. Klein, "The infinite PCFG using hierarchical Dirichlet processes," in Proc. 2007 Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2007), East Stroudsburg, PA: The Association for Computational Linguistics, 2007, pp. 688-697.
J. Nilsson, F. Sha, and M. Jordan, "Regression on manifolds using kernel dimension reduction," in Proc. 24th Intl. Conf. on Machine Learning (ICML '07), Z. Ghahramani, Ed., ACM International Conference Proceeding Series, Vol. 227, New York, NY: The Association for Computing Machinery, Inc., 2007, pp. 697-704.
L. Huang, X. Nguyen, M. Garofalakis, J. M. Hellerstein, M. Jordan, A. D. Joseph, and N. Taft, "Communication-efficient online detection of network-wide anomalies," in Proc. 26th IEEE Intl. Conf. on Computer Communications (INFOCOM 2007), Piscataway, NJ: IEEE Press, 2007, pp. 134-142.
R. Thibaux and M. Jordan, "Hierarchical beta processes and the Indian buffet process," in Proc. 11th Intl. Conf. on Artificial Intelligence and Statistics (AISTATS 2007), M. Meila and X. Shen, Eds., Madison, WI: Omnipress, 2007, pp. online.
Z. Zhang and M. Jordan, "Bayesian multicategory support vector machines," in Proc. 22nd Conf. on Uncertainty in Artificial Intelligence (UAI 2006), Arlington, VA: AUAI Press, 2006, pp. 8 pg.
P. Bodik, A. Fox, M. Jordan, D. A. Patterson, A. Banerjee, R. Jagannathan, T. Su, S. Tenginakai, B. Turner, and J. Ingalls, "Advanced tools for operators at Amazon.com," in Proc. 1st Workshop on Hot Topics in Autonomic Computing (HotAC I), Piscataway, NJ: IEEE Press, 2006, pp. 5 pg.
S. Lacoste Julien, B. Taskar, D. Klein, and M. Jordan, "Word alignment via quadratic assignment," in Proc. 4th Human Language Technology Conf. of the North American Chapter of the Association for Computational Linguistics Annual Meeting (HLT-NAACL 2006), R. C. Moore, J. A. Bilmes, J. Chu Carroll, and M. Sanderson, Eds., East Stroudsburg, PA: Association for Computational Linguistics, 2006, pp. 112-119.
B. E. Engelhardt, M. Jordan, and S. E. Brenner, "A graphical model for predicting protein molecular function," in Proc. 23rd Intl. Conf. on Machine Learning (ICML '06), W. W. Cohen and A. Moore, Eds., ACM International Conference Proceeding, Vol. 148, New York, NY: The Association for Computing Machinery, Inc., 2006.
A. X. Zheng, M. Jordan, B. Liblit, M. Naik, and A. Aiken, "Statistical debugging: Simultaneous identification of multiple bugs," in Proc. 23rd Intl. Conf. on Machine Learning (ICML '06), W. W. Cohen and A. Moore, Eds., ACM International Conference Proceeding, Vol. 148, New York, NY: The Association for Computing Machinery, Inc., 2006, pp. 1105-1112.
E. P. Xing, K. A. Sohn, M. Jordan, and Y. W. Teh, "Bayesian multi-population haplotype inference via a hierarchical Dirichlet process mixture," in Proc. 23rd Intl. Conf. on Machine Learning (ICML '06), W. W. Cohen and A. Moore, Eds., ACM International Conference Proceeding, Vol. 148, New York, NY: The Association for Computing Machinery, Inc., 2006, pp. 1049-1056.
F. R. Bach and M. Jordan, "Predictive low-rank decomposition for kernel methods," in Proc. 22nd Intl. Conf. on Machine Learning (ICML '05), L. De Raedt and S. Wrobel, Eds., ACM International Conference Proceeding, Vol. 119, New York, NY: The Association for Computing Machinery, Inc., 2005, pp. 33-40.
M. Rozen Zvi, M. Jordan, and A. Yuille, "The DLR hierarchy of approximate inference," in Proc. 21st Conf. in Uncertainty in Artificial Intelligence (UAI 2005), Arlington, VA: AUAI Press, 2005, pp. 493-500.
B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. Jordan, "Scalable statistical bug isolation," in Proc. 2005 ACM SIGPLAN Conf. on Programming Language Design and Implementation (PLDI '05), New York, NY: The Association for Computing Machinery, Inc., 2005, pp. 15-26.
Y. W. Teh, M. Seeger, and M. Jordan, "Semiparametric latent factor models," in Proc. 10th Intl. Workshop on Artificial Intelligence and Statistics (AISTATS 2005), R. Cowell and Z. Ghahramani, Eds., NJ: The Society for Artificial Intelligence and Statistics, 2005, pp. 333-340.
M. Wainwright and M. Jordan, "Variational inference in graphical models: The view from the marginal polytope," in Proc. 41st Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, IL: University of Illinois, 2004.
E. P. Xing, M. Jordan, and S. J. Russell, "Graph partition strategies for generalized mean field inference," in Proc. 20th Conf. on Uncertainty in Artificial Intelligence (UAI-2004), M. Chickering and J. Halpern, Eds., Arlington, VA: AUAI Press, 2004, pp. 602-610.
D. M. Blei and M. Jordan, "Variational methods for the Dirichlet process," in Proc. 21st Intl. Conf. on Machine Learning (ICML '04), ACM International Conference Proceeding Series, Vol. 69, New York, NY: The Association for Computing Machinery, Inc., 2004, pp. Art. 12.
X. Nguyen, M. Wainwright, and M. Jordan, "Decentralized detection and classification using kernel methods," in Proc. 21st International Conference on Machine Learning (ICML '04), ACM International Conference Proceeding Series, Vol. 69, New York, NY: The Association for Computing Machinery, Inc., 2004, pp. Art. 80.
E. Xing, R. Sharan, and M. Jordan, "Bayesian haplotype inference via the Dirichlet process," in Proc. 21st Intl. Conf. on Machine Learning (ICML '04), Vol. 69, New York, NY: The Association for Computing Machinery, Inc., 2004, pp. Art. 111.
F. R. Bach, G. R. G. Lanckriet, and M. Jordan, "Multiple kernel learning, conic duality, and the SMO algorithm," in Proc. 21st Intl. Conf. on Machine Learning (ICML '04), ACM International Conference Proceeding Series, Vol. 69, New York, NY: The Association for Computing Machinery, Inc., 2004, pp. Art. 6.
B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. Jordan, "Public deployment of cooperative bug isolation," in Proc. 2nd ICSE Workshop on Remote Analysis and Measurement of Software Systems (RAMSS '04), Stevenage, UK: IEE Society Press, 2004, pp. 57-62.
M. Chen, A. X. Zheng, J. Lloyd, M. Jordan, and E. Brewer, "Failure diagnosis using decision trees," in Proc. 1st Intl. Conf. on Autonomic Computing (ICAC 2004), Los Alamitos, CA: IEEE Computer Society Press, 2004, pp. 36-43.
G. R. G. Lanckriet, M. Deng, N. Cristianini, M. Jordan, and W. S. Noble, "Kernel-based data fusion and its application to protein function prediction in yeast," in Biocomputing 2004: Proc. Pacific Symp., Hawaii (PSB 2004), R. B. Altman, A. K. Dunker, L. Hunter, T. A. Jung, and T. E. Klein, Eds., Hoboken, NJ: World Scientific, 2004, pp. 300-311.
B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, and S. S. Sastry, "Kalman filtering with intermittent observations," in Proc. 42nd IEEE Intl. Conf. on Decision and Control (CDC 2003), Vol. 1, Piscataway, NJ: IEEE Press, 2003, pp. 701-708.
E. P. Xing, W. Wu, M. Jordan, and R. M. Karp, "LOGOS: A modular Bayesian model for de novo motif detection," in Proc. 2nd Intl. IEEE Computer Society Computational Systems Bioinformatics Conf. (CSB 2003), Los Alamitos, CA: IEEE Computer Society, 2003, pp. 266-276.
N. de Freitas, P. A. d. F. R. Hojen-Sorensen, M. Jordan, and S. J. Russell, "Variational MCMC," in Proc. 17th Conf. on Uncertainty in Artificial Intelligence (UAI-2001), J. S. Breese and D. Koller, Eds., San Francisco, CA: Morgan Kaufmann, 2001, pp. 120-127.
E. P. Xing, M. Jordan, and R. M. Karp, "Feature selection for high-dimensional genomic microarray data," in Proc. 18th Intl. Conf. on Machine Learning (ICML '01), C. E. Brodley and A. P. Danyluk, Eds., San Francisco, CA: Morgan Kaufmann Publishers Inc., 2001, pp. 601-608.
I. Stoica, M. Franklin, M. Jordan, A. Fox, A. D. Joseph, M. Mahoney, R. H. Katz, D. A. Patterson, and S. Shenker, "The Berkeley Data Analysis System (BDAS): An open source platform for big data analytics," University of California, Berkeley Berkeley United States, 2017.
I. Stoica, D. Song, R. A. Popa, D. A. Patterson, M. W. Mahoney, R. H. Katz, A. D. Joseph, M. Jordan, J. M. Hellerstein, J. Gonzalez, K. Goldberg, A. Ghodsi, D. E. Culler, and P. Abbeel, "A Berkeley View of Systems Challenges for AI," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2017-159, Oct. 2017.
D. Haussler, D. A. Patterson, M. Diekhans, A. Fox, M. Jordan, A. D. Joseph, S. Ma, B. Paten, S. Shenker, T. Sittler, and I. Stoica, "A Million Cancer Genome Warehouse," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2012-211, Nov. 2012.
J. Duchi, L. Mackey, and M. Jordan, "On the Consistency of Ranking Algorithms," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2010-56, May 2010.
D. Yan, L. Huang, and M. Jordan, "Fast Approximate Spectral Clustering," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-45, March 2009.
L. Huang, X. Nguyen, M. Garofalakis, M. Jordan, A. D. Joseph, and N. Taft, "In-Network PCA and Anomaly Detection," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2007-10, Jan. 2007.
K. Fukumizu, F. R. Bach, and M. Jordan, "Kernel Dimension Reduction in Regression," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-06-715, Sep. 2006.
L. Huang, X. Nguyen, M. Garofalakis, M. Jordan, A. D. Joseph, and N. Taft, "Distributed PCA and Network Anomaly Detection," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2006-99, July 2006.
Y. W. Teh, M. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical Dirichlet Processes," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-04-653, Oct. 2004.
F. R. Bach and M. I. Jordan, "Learning Spectral Clustering," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-03-1249, June 2003.
B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, and S. S. Sastry, "Kalman Filtering with Intermittent Observations," EECS Department, University of California, Berkeley, Tech. Rep. UCB/ERL M03/15, May 2003.
P. Bartlett, M. Jordan, and J. D. McAuliffe, "Convexity, Classification, and Risk Bounds," University of California, Department of Statistics, Tech. Rep. UCB/STAT-04-638, April 2003.
G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan, "Learning the Kernel Matrix with Semi-Definite Programming," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-02-1206, 2002.
G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. I. Jordan, "A Robust Minimax Approach to Classification," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-02-1218, Dec. 2002.
F. R. Bach and M. I. Jordan, "Kernel Independent Component Analysis," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-01-1166, Nov. 2001.
A. Bouchard-Cote, M. Jordan, D. Klein, T. L. Griffiths, and Y. S. Song, "Probabilistic Models of Evolution and Language Change," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2010-153, Dec. 2010.