Publication:
2017
- Retinopathy Signs Improved Prediction and Reclassification of Cardiovascular Disease Risk in Diabetes: A prospective cohort study Ho H, Cheung CY, Sabanayagam C, Yip W, Ikram MK, Ong PG, Mitchell P, Chow KY, Cheng CY, Tai ES, Wong TY Sci Rep. 2017 Feb 2;7:41492. doi: 10.1038/srep41492.
- Prevalence, Risk Factors, and Impact of Undiagnosed Visually Significant Cataract: The Singapore Epidemiology of Eye Diseases Study. Chua J, Lim B, Fenwick EK, Gan AT, Tan AG, Lamoureux E, Mitchell P, Wang JJ, Wong TY, Cheng CY PLoS One. 2017 Jan 27;12(1):e0170804. doi: 10.1371/journal.pone.0170804.
- Association of Systemic Medication Use With Intraocular Pressure in a Multiethnic Asian Population: The Singapore Epidemiology of Eye Diseases Study. Chua J, Lim B, Fenwick EK, Gan AT, Tan AG, Lamoureux E, Mitchell P, Wang JJ, Wong TY, Cheng CY
arXiv:1611.09981, 2016. - Review: Imaging retina to study dementia and stroke. Cheung CY, Ikram MK, Chen C, Wong TY Prog Retin Eye Res. 2017 Jan 3. pii: S1350-9462(16)30065-9. doi: 10.1016/j.preteyeres.2017.01.001
- Characterisation of choroidal morphological and vascular features in diabetes and diabetic retinopathy. Gupta P, Thakku SG, Sabanayagam C, Tan G, Cheung CM, Lamoureux EL, Wong TY, Cheng CY Br J Ophthalmol. 2017 Jan 5. pii: bjophthalmol-2016-309366. doi: 10.1136/bjophthalmol-2016-309366
2016
- Type 2 Diabetes Genetic Variants and Risk of Diabetic Retinopathy. A. El Alaoui, A. Ramdas, F. Krzakala, L. Zdeborova, and M. I. Jordan.
arXiv:1611.09981, 2016.
A variational perspective on accelerated methods in optimization. A. Wibisono, A. Wilson, and M. I. Jordan.
Proceedings of the National Academy of Sciences, 133, E7351-E7358, 2016.
[ArXiv version]
On the computational complexity of high-dimensional Bayesian variable selection.Y. Yang, M. Wainwright, and M. I. Jordan.
Annals of Statistics, 44, 2497-2532, 2016.
A Lyapunov analysis of momentum methods in optimization. A. Wilson, B. Recht and M. I. Jordan.
arXiv:1611.02635, 2016.
Less than a single pass: Stochastically controlled stochastic gradient method. Lei, L., and M. I. Jordan.
arXiv:1609.03261, 2016.
CoCoA: A general framework for communication-efficient distributed optimization. V. Smith, S. Forte, C. Ma, M. Takac, M. I. Jordan, and M. Jaggi.
arXiv:1611.02189, 2016.
Communication-efficient distributed statistical inference. M. I. Jordan, J. Lee, and Y. Yang.
arXiv:1605.07689, 2016.
Saturating splines and feature selection. Boyd, N., Hastie, T., Boyd, S., Recht, B., and M. I. Jordan.
arXiv:1609.06764, 2016.
Function-specific mixing times and concentration away from equilibrium.
M. Rabinovich, A. Ramdas, M. I. Jordan, and M. Wainwright.
arXiv:1605.02077, 2016.
Fast robustness quantification with variational Bayes.
R. Giordano, T. Broderick, R. Meager, J. Huggins, and M. I. Jordan.
arXiv:1606.07153, 2016.
CYCLADES: Conflict-free asynchronous machine learning.
X. Pan, M. Lam, S. Tu, D. Papailiopoulos, C. Zhang, M. I. Jordan,
K. Ramchandran, C. Re, and B. Recht.
arXiv:1605.09721, 2016.
Local maxima in the likelihood of Gaussian mixture models: Structural
results and algorithmic consequences.
C. Jin, Y. Zhang, S. Balakrishnan, M. J. Wainwright, and M. I. Jordan.
arXiv:1609.00978, 2016.
Deep transfer learning with joint adaptation networks.
M. Long, J. Wang, and M. I. Jordan.
arXiv:1605.06636, 2016.
A constructive definition of the beta process.
J. Paisley and M. I. Jordan.
arXiv:1604.00685, 2016.
Universality of Mallows’ and degeneracy of Kendall’s kernels for rankings.
H. Mania, A. Ramdas, M. Wainwright, M. I. Jordan and B. Recht.
arXiv:1603.04245, 2016.
Spectral methods meet EM: A provably optimal algorithm for crowdsourcing.
Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan.
Journal of Machine Learning Research, 101, 1-44, 2016.
Gradient descent converges to minimizers.
J. Lee, M. Simchowitz, M. I. Jordan, and B. Recht.
Proceedings of the Conference on Computational Learning Theory (COLT),
New York, NY, 2016.
Asymptotic behavior of l_p-based Laplacian regularization in
semi-supervised learning.
A. El Alaoui, X. Cheng, A. Ramdas, M. Wainwright and M. I. Jordan.
Proceedings of the Conference on Computational Learning Theory (COLT),
New York, NY, 2016.
A kernelized Stein discrepancy for goodness-of-fit tests and model evaluation.
Q. Liu, J. Lee, and M. I. Jordan.
Proceedings of the 33rd International Conference on Machine
Learning (ICML), New York, NY, 2016.
l_1-regularized neural networks are improperly learnable in polynomial time.
Y. Zhang, J. Lee, and M. I. Jordan.
Proceedings of the 33rd International Conference on Machine
Learning (ICML), New York, NY, 2016.
A linearly-convergent stochastic L-BFGS algorithm.
P. Moritz, R. Nishihara, and M. I. Jordan.
Proceedings of the Eighteenth Conference on Artificial
Intelligence and Statistics (AISTATS), Cadiz, Spain, 2016.
Posteriors, conjugacy, and exponential families for completely random measures.
T. Broderick, A. Wilson, and M. I. Jordan.
Bernoulli, to appear.
High-dimensional continuous control using generalized advantage estimation.
J. Schulman, P. Moritz, S. Levine, M. I. Jordan, and P. Abbeel.
International Conference on Learning Representations (ICLR),
Puerto Rico, 2016.
SparkNet: Training deep networks in Spark.
P. Moritz, R. Nishihara, I. Stoica and M. I. Jordan.
International Conference on Learning Representations (ICLR),
Puerto Rico, 2016.
The constrained Laplacian rank algorithm for graph-based clustering.
F. Nie, X. Wang, M. I. Jordan, H. Huang.
In Proceedings of the Thirtieth Conference on Artificial Intelligence (AAAI),
Phoenix, AZ, 2016.
Parallel correlation clustering on big graphs.
X. Pan, D. Papailiopoulos, S. Oymak, B. Recht, K. Ramchandran, and M. I. Jordan.
In D. Lee, M. Sugiyama, C. Cortes and N. Lawrence (Eds.),
Advances in Neural Information Processing Systems (NIPS) 27, 2016.
On the accuracy of self-normalized log-linear models.
J. Andreas, M. Rabinovich, D. Klein, and M. I. Jordan.
In D. Lee, M. Sugiyama, C. Cortes and N. Lawrence (Eds.),
Advances in Neural Information Processing Systems (NIPS) 27, 2016.
Variational consensus Monte Carlo.
M. Rabinovich, E. Angelino, and M. I. Jordan.
In D. Lee, M. Sugiyama, C. Cortes and N. Lawrence (Eds.),
Advances in Neural Information Processing Systems (NIPS) 27, 2016.
Linear response methods for accurate covariance estimates from mean field
variational Bayes.
R. Giordano, T. Broderick, and M. I. Jordan.
In D. Lee, M. Sugiyama, C. Cortes and N. Lawrence (Eds.),
Advances in Neural Information Processing Systems (NIPS) 27, 2016.
2015
Machine learning: Trends, perspectives, and prospects.
M. I. Jordan and T. Mitchell.
Science, 349, 255-260, 2015.
Nested hierarchical Dirichlet processes.
J. Paisley, C. Wang, D. Blei, and M. I. Jordan.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
37, 256-270, 2015.
Combinatorial clustering and the beta negative binomial process.
T. Broderick, L. Mackey, J. Paisley and M. I. Jordan.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
37, 290-306, 2015.
Distributed matrix completion and robust factorization.
L. Mackey, A. Talwalkar and M. I. Jordan.
Journal of Machine Learning Research, 16, 913-960, 2015.
Optimal rates for zero-order optimization: the power of two function evaluations.
J. Duchi, M. I. Jordan, M. Wainwright, and A. Wibisono.
IEEE Transactions on Information Theory, 61, 2788-2806, 2015.
Perturbed iterate analysis for asynchronous stochastic optimization.
H. Mania, X. Pan, D. Papailiopoulos, B. Recht, K. Ramchandran, and M. I. Jordan.
arXiv:1507.06970, 2015.
Robust inference with variational Bayes.
R. Giordano, T. Broderick, and M. I. Jordan.
arXiv:1512.02578, 2015.
Learning halfspaces and neural networks with random initialization.
Y. Zhang, J. Lee, M. Wainwright and M. I. Jordan.
arXiv:1511.07948, 2015.
Asynchronous complex analytics in a distributed dataflow architecture.
J. Gonzalez, P. Bailis, M. I. Jordan, M. Franklin, J. Hellerstein, A. Ghodsi, and I. Stoica.
arXiv:1510.07092, 2015.
Splash: User-friendly programming interface for parallelizing stochastic algorithms.
Y. Zhang and M. I. Jordan.
arXiv:1506.07552, 2015.
Trust region policy optimization.
J. Schulman, P. Moritz, S. Levine, M. I. Jordan, and P. Abbeel.
In F. Bach and D. Blei (Eds.),
Proceedings of the 32nd International Conference on Machine
Learning (ICML), Lille, France, 2015.
[Long version]
Adding vs. averaging in distributed primal-dual optimization.
C. Ma, V. Smith, M. Jaggi, M. I. Jordan, P. Richtarik, and M. Takac.
In F. Bach and D. Blei (Eds.),
Proceedings of the 32nd International Conference on Machine
Learning (ICML), Lille, France, 2015.
[Long version]
A general analysis of the convergence of ADMM.
R. Nishihara, L. Lessart, B. Recht, A. Packard, and M. I. Jordan.
In F. Bach and D. Blei (Eds.),
Proceedings of the 32nd International Conference on Machine
Learning (ICML), Lille, France, 2015.
[Long version]
Learning transferable features with deep adaptation networks.
M. Long, Y. Cao, J. Wang, and M. I. Jordan.
In F. Bach and D. Blei (Eds.),
Proceedings of the 32nd International Conference on Machine
Learning (ICML), Lille, France, 2015.
Distributed estimation of generalized matrix rank: Efficient algorithms and lower bounds.
Y. Zhang, M. Wainwright, and M. I. Jordan.
In F. Bach and D. Blei (Eds.),
Proceedings of the 32nd International Conference on Machine
Learning (ICML), Lille, France, 2015.
Automating model search for large scale machine learning.
E. Sparks, A. Talwalkar, D. Haas, M. Franklin, M. I. Jordan, and T. Kraska.
ACM Symposium on Cloud Computing (SOCC), Kohala Coast, Hawaii, 2015.
Optimal prediction for sparse linear models? Lower bounds for
coordinate-separable M-estimators.
Y. Zhang, M. Wainwright, and M. I. Jordan.
arXiv:1502.03188, 2015.
TuPAQ: An efficient planner for large-scale predictive analytic queries.
E. Sparks, A. Talwalkar, M. J. Franklin, M. I. Jordan, and T. Kraska.
arXiv:1502.00068, 2015.
Spectral methods meet EM: A provably optimal algorithm for crowdsourcing.
Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan.
In Z. Ghahramani, M. Welling, C. Cortes and N. Lawrence (Eds.),
Advances in Neural Information Processing Systems (NIPS) 27, 2015.
On the convergence rate of decomposable submodular function minimization.
R. Nishihara, S. Jegelka, and M. I. Jordan.
In Z. Ghahramani, M. Welling, C. Cortes and N. Lawrence (Eds.),
Advances in Neural Information Processing Systems (NIPS) 27, 2015.
Communication-efficient distributed dual coordinate ascent.
M. Jaggi, V. Smith, M. Takac, J. Terhorst, T. Hofmann, and M. I. Jordan.
In Z. Ghahramani, M. Welling, C. Cortes and N. Lawrence (Eds.),
Advances in Neural Information Processing Systems (NIPS) 27, 2015.
Parallel double greedy submodular maximization.
X. Pan, S. Jegelka, J. Gonzalez, J. Bradley, and M. I. Jordan.
In Z. Ghahramani, M. Welling, C. Cortes and N. Lawrence (Eds.),
Advances in Neural Information Processing Systems (NIPS) 27, 2015.
Optimism-driven exploration for nonlinear systems.
T. Moldovan, S. Levine, M. I. Jordan, and P. Abbeel.
In IEEE International Conference on Robotics and Automation (ICRA),
Seattle, WA, 2015.
2014
Matrix concentration inequalities via the method of exchangeable pairs.
L. Mackey, M. I. Jordan, R. Y. Chen, B. Farrell and J. A. Tropp.
Annals of Probability, 42, 906-945, 2014.
A scalable bootstrap for massive data.
A. Kleiner, A. Talwalkar, P. Sarkar and M. I. Jordan.
Journal of the Royal Statistical Society, Series B,
76, 795-816, 2014.
Privacy aware learning.
J. Duchi, M. I. Jordan, and M. Wainwright.
Journal of the ACM, 61, http://dx.doi.org/10.1145/2666468, 2014.
Joint modeling of multiple time series via the beta process with
application to motion capture segmentation.
E. Fox, M. Hughes, E. Sudderth, and M. I. Jordan.
Annals of Applied Statistics, 8, 1281-1313, 2014.
Nonparametric link prediction in large scale dynamic networks.
P. Sarkar, D. Chakrabarti, and M. I. Jordan.
Electronic Journal of Statistics, 8, 2022-2065, 2014.
Particle Gibbs with ancestral sampling.
F. Lindsten, M. I. Jordan, and T. Sch�n.
Journal of Machine Learning Research,
15, 2145-2184, 2014.
Iterative discovery of multiple alternative clustering views.
D. Niu, J. Dy, and M. I. Jordan.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
36, 1340-1353, 2014.
Matrix-variate Dirichlet process priors with applications.
Z. Zhang, D. Wang, G. Dai, and M. I. Jordan.
Bayesian Analysis, 9, 259-286, 2014.
SMASH: A benchmarking toolkit for variant calling.
A. Talwalkar, J. Liptrap, J. Newcomb, C. Hartl, J. Terhorst, K. Curtis, M Bresler,
Y. Song, M. I. Jordan, and D. Patterson.
Bioinformatics,
DOI:10.1093/bioinformatics/btu345, 2014.
Optimality guarantees for distributed statistical estimation.
J. Duchi, M. I. Jordan, M. Wainwright, and Y. Zhang.
arXiv:1405.0782, 2014.
The missing piece in complex analytics: Low latency, scalable model
management and serving with Velox.
D. Crankshaw, P. Bailis, J. E. Gonzalez, H. Li, Z. Zhang, M. J. Franklin,
A. Ghodsi, and M. I. Jordan.
Conference on Innovative Data Systems Research (CIDR),
Asilomar, CA, 2014.
Lower bounds on the performance of polynomial-time algorithms
for sparse linear regression.
Y. Zhang, M. Wainwright, and M. I. Jordan.
Proceedings of the Conference on Computational Learning Theory (COLT),
Barcelona, Spain, 2014.
Knowing when you’re wrong: Building fast and reliable approximate
query processing systems.
S. Agarwal, H. Milner, A. Kleiner, B. Mozafari, M. I. Jordan,
S. Madden, and I. Stoica.
Proceedings of the 2014 ACM International Conference on Management
of Data (SIGMOD), Snowbird, Utah, 2014.
Scaling a crowd-sourced database.
B. Mozafari, P. Sarkar, M. Franklin, M. I. Jordan, and S. Madden.
Proceedings of the 41st International Conference on Very Large Data Bases (VLDB),
Hawaii, USA, 2014.
Changepoint analysis for efficient variant calling.
A. Bloniarz, A. Talwalkar, J. Terhorst, M. I. Jordan, D. Patterson,
B. Yu, and Y. Song.
International Conference on Research in Computational
Molecular Biology (RECOMB), Pittsburgh, PA, 2014.
Mixed membership models for time series.
E. Fox and M. I. Jordan.
In E. Airoldi, D. Blei, E. A. Erosheva, and S. E. Fienberg (Eds.),
Handbook of Mixed Membership Models and Their Applications,
Chapman & Hall/CRC, 2014.
Mixed membership matrix factorization.
L. Mackey, D. Weiss, and M. I. Jordan.
In E. Airoldi, D. Blei, E. A. Erosheva, and S. E. Fienberg (Eds.),
Handbook of Mixed Membership Models and Their Applications,
Chapman & Hall/CRC, 2014.
Bayesian nonnegative matrix factorization with stochastic variational inference.
J. Paisley, D. Blei, and M. I. Jordan.
In E. Airoldi, D. Blei, E. A. Erosheva, and S. E. Fienberg (Eds.),
Handbook of Mixed Membership Models and Their Applications,
Chapman & Hall/CRC, 2014.
Optimistic concurrency control for distributed unsupervised learning.
X. Pan, J. Gonzalez, S. Jegelka, T. Broderick, and M. I. Jordan.
In L. Bottou, C. Burges, Z. Ghahramani, and M. Welling (Eds.),
Advances in Neural Information Processing Systems (NIPS) 26, 2014.
Information-theoretic lower bounds for distributed statistical estimation
with communication constraints.
Y. Zhang, J. Duchi, M. I. Jordan, and M. Wainwright.
In L. Bottou, C. Burges, Z. Ghahramani, and M. Welling (Eds.),
Advances in Neural Information Processing Systems (NIPS) 26, 2014.
Estimation, optimization, and parallelism when data is sparse.
J. Duchi, M. I. Jordan, and B. McMahan.
In L. Bottou, C. Burges, Z. Ghahramani, and M. Welling (Eds.),
Advances in Neural Information Processing Systems (NIPS) 26, 2014.
Streaming variational Bayes.
T. Broderick, N. Boyd, A. Wibisono, A. Wilson and M. I. Jordan.
In L. Bottou, C. Burges, Z. Ghahramani, and M. Welling (Eds.),
Advances in Neural Information Processing Systems (NIPS) 26, 2014.
Local privacy and minimax bounds: Sharp rates for probability estimation.
J. Duchi, M. I. Jordan, and M. Wainwright
In L. Bottou, C. Burges, Z. Ghahramani, and M. Welling (Eds.),
Advances in Neural Information Processing Systems (NIPS) 26, 2014.
A comparative framework for preconditioned Lasso algorithms.
F. Wauthier, N. Jojic and M. I. Jordan.
In L. Bottou, C. Burges, Z. Ghahramani, and M. Welling (Eds.),
Advances in Neural Information Processing Systems (NIPS) 26, 2014.
2013
Learning dependency-based compositional semantics.
P. Liang, M. I. Jordan, and D. Klein.
Computational Linguistics, 39, 389-446, 2013.
Computational and statistical tradeoffs via convex relaxation.
V. Chandrasekaran and M. I. Jordan.
Proceedings of the National Academy of Sciences, 110, E1181-E1190, 2013.
Feature allocations, probability functions, and paintboxes.
T. Broderick, J. Pitman, and M. I. Jordan.
Bayesian Analysis, 8, 801-836, 2013.
On statistics, computation and scalability.
M. I. Jordan.
Bernoulli, 19, 1378-1390, 2013.
The asymptotics of ranking algorithms.
J. Duchi, L. Mackey, and M. I. Jordan.
Annals of Statistics, 4, 2292-2323, 2013.
Evolutionary inference via the Poisson indel process.
A. Bouchard-C�t� and M. I. Jordan.
Proceedings of the National Academy of Sciences, 110, 1160-1166, 2013.
Clusters and features from combinatorial stochastic processes.
T. Broderick, M. I. Jordan, and J. Pitman.
Statistical Science, 28, 289-312, 2013.
Bayesian semiparametric Wiener system identification.
F. Lindsten, T. Sch�n, and M. I. Jordan.
Automatica, 49, 2053-2063, 2013.
Cluster forests.
D. Yan, A. Chen, and M. I. Jordan.
Computational Statistics and Data Analysis, 66, 178-192, 2013.
Molecular function prediction for a family exhibiting evolutionary tendencies
towards substrate specificity swapping: Recurrence of tyrosine aminotransferase
activity in the I$\alpha$ subfamily.
K. Muratore, B. Engelhardt, J. Srouji, M. I. Jordan, S. Brenner, and J. Kirsch.
Proteins: Structure, Function, and Bioinformatics, DOI:10.1002/prot.24318, 2013.
Local privacy, data processing inequalities, and statistical minimax rates.
J. Duchi, M. I. Jordan, and M. Wainwright.
arXiv:1302.3203, 2013.
MLI: An API for distributed machine learning.
E. Sparks, A. Talwalkar, V. Smith, J. Kottalam, X. Pan, J. Gonzalez, M. I. Jordan,
M. Franklin, and T. Kraska. IEEE International Conference on Data Mining (ICDM),
Dallas, TX, 2013.
MAD-Bayes: MAP-based asymptotic derivations from Bayes.
T. Broderick, B. Kulis, and M. I. Jordan.
In S. Dasgupta and D. McAllester (Eds.),
Proceedings of the 30th International Conference on Machine
Learning (ICML), Atlanta, GA, 2013.
[Supplementary information].
Efficient ranking from pairwise comparisons.
F. Wauthier, M. I. Jordan, and N. Jojic.
In S. Dasgupta and D. McAllester (Eds.),
Proceedings of the 30th International Conference on Machine
Learning (ICML), Atlanta, GA, 2013.
[Supplementary information].
Distributed low-rank subspace segmentation.
L. Mackey, A. Talwalkar, Y. Mu, S-F. Chang, and M. I. Jordan.
IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 2013.
A general bootstrap performance diagnostic.
A. Kleiner, A. Talwalkar, S. Agarwal, M. I. Jordan, and I. Stoica.
ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), Chicago, IL, 2013.
Local privacy and minimax bounds: Sharp rates for probability estimation.
J. Duchi, M. I. Jordan, and M. Wainwright.
arXiv:1305.6000, 2013.
Small-variance asymptotics for exponential family Dirichlet process mixture models.
K. Jiang, B. Kulis, and M. I. Jordan.
In P. Bartlett, F. Pereira, L. Bottou and C. Burges (Eds.),
Advances in Neural Information Processing Systems (NIPS) 25, 2013.
Ancestral sampling for particle Gibbs.
F. Lindsten, M. I. Jordan, and T. Sch�n.
In P. Bartlett, F. Pereira, L. Bottou and C. Burges (Eds.),
Advances in Neural Information Processing Systems (NIPS) 25, 2013.
Finite sample convergence rates of zero-order stochastic optimization methods.
J. Duchi, M. I. Jordan, M. Wainwright, and A. Wibisono.
In P. Bartlett, F. Pereira, L. Bottou and C. Burges (Eds.),
Advances in Neural Information Processing Systems (NIPS) 25, 2013.
Privacy aware learning.
J. Duchi, M. I. Jordan, and M. Wainwright.
In P. Bartlett, F. Pereira, L. Bottou and C. Burges (Eds.),
Advances in Neural Information Processing Systems (NIPS) 25, 2013.
[Long version].
2012
Phylogenetic inference via sequential Monte Carlo.
A. Bouchard-C�t�, S. Sankararaman, and M. I. Jordan.
Systematic Biology, 61, 579-593, 2012.
Ergodic mirror descent.
J. C. Duchi, A. Agarwal, M. Johansson, and M. I. Jordan.
SIAM Journal of Optimization, 22, 1549-1578, 2012.
EP-GIG priors and applications in Bayesian sparse learning.
Z. Zhang, S. Wang, D. Liu, and M. I. Jordan.
Journal of Machine Learning Research, 13, 2031-2061, 2012.
Beta processes, stick-breaking, and power laws.
T. Broderick, M. I. Jordan and J. Pitman.
Bayesian Analysis, 7, 439-476, 2012.
Coherence functions with applications in large-margin classification methods.
Z. Zhang, D. Liu, G. Dai, and M. I. Jordan.
Journal of Machine Learning Research, 13, 2705-2734, 2012.
A million cancer genome warehouse.
D. Haussler, D. A. Patterson, M. Diekhans, A. Fox, M. I. Jordan, A. D. Joseph,
S. Ma, B. Paten, S. Shenker, T. Sittler and I. Stoica.
Technical Report UCB/EECS-2012-211, Department of EECS,
University of California, Berkeley, 2012.
Active learning for crowd-sourced databases.
B. Mozafari, P. Sarkar, M. J. Franklin, M. I. Jordan, and S. Madden.
arXiv:1209.3686, 2012.
The Big Data bootstrap.
A. Kleiner, A. Talwalkar, P. Sarkar, and M. I. Jordan.
In J. Langford and J. Pineau (Eds.),
Proceedings of the 29th International Conference on Machine
Learning (ICML), Edinburgh, UK, 2012.
Revisiting k-means: New algorithms via Bayesian nonparametrics.
B. Kulis and M. I. Jordan.
In J. Langford and J. Pineau (Eds.),
Proceedings of the 29th International Conference on Machine
Learning (ICML), Edinburgh, UK, 2012.
Variational Bayesian inference with stochastic search.
J. Paisley, D. Blei, and M. I. Jordan.
In J. Langford and J. Pineau (Eds.),
Proceedings of the 29th International Conference on Machine
Learning (ICML), Edinburgh, UK, 2012.
Nonparametric link prediction in dynamic networks.
P. Sarkar, D. Chakrabarti, and M. I. Jordan.
In J. Langford and J. Pineau (Eds.),
Proceedings of the 29th International Conference on Machine
Learning (ICML), Edinburgh, UK, 2012.
[Appendix].
Stick-breaking beta processes and the Poisson process.
J. Paisley, D. Blei, and M. I. Jordan.
In N. Lawrence and M. Girolami (Eds.),
Proceedings of the Fifteenth Conference on Artificial Intelligence
and Statistics (AISTATS), Canary Islands, Spain, 2012.
A semiparametric Bayesian approach to Wiener system identification.
F. Lindsten, T. Sch�n, and M. I. Jordan.
16th IFAC Symposium on System Identification (SYSID), Brussels, Belgium, 2012.
Active spectral clustering via iterative uncertainty reduction.
F. Wauthier, N. Jojic, and M. I. Jordan.
18th ACM Conference on Knowledge Discovery and Data Mining
(SIGKDD), Beijing, China, 2012.
Bayesian bias mitigation for crowdsourcing.
F. L. Wauthier and M. I. Jordan.
In J. Shawe-Taylor, R. Zemel, P. Bartlett and F. Pereira (Eds.),
Advances in Neural Information Processing Systems (NIPS) 24, 2012.
Divide-and-conquer matrix factorization.
L. Mackey, A. Talwalkar and M. I. Jordan.
In J. Shawe-Taylor, R. Zemel, P. Bartlett and F. Pereira (Eds.),
Advances in Neural Information Processing Systems (NIPS) 24, 2012.
[Long version].
2011
Union support recovery in high-dimensional multivariate regression.
G. Obozinski, M. J. Wainwright, and M. I. Jordan.
Annals of Statistics, 39, 1-47, 2011.
Bayesian inference for queueing networks and modeling of Internet services.
C. Sutton and M. I. Jordan.
Annals of Applied Statistics, 5, 254-282, 2011.
Genome-scale phylogenetic function annotation of large and
diverse protein families.
B. Engelhardt, M. I. Jordan, J. Srouji, and S. Brenner.
Genome Research, 21, 1969-1980, 2011.
A sticky HDP-HMM with application to speaker diarization.
E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky.
Annals of Applied Statistics, 5, 1020-1056, 2011.
Learning low-dimensional signal models.
L. Carin, R. G. Baraniuk, V. Cevher, D. Dunson, M. I. Jordan, G. Sapiro,
and M. B. Wakin.
IEEE Signal Processing Magazine, 28, 39-51, 2011.
Bayesian generalized kernel mixed models.
Z. Zhang, G. Dai, and M. I. Jordan.
Journal of Machine Learning Research, 12, 111-139, 2011.
Bayesian nonparametric inference of switching linear dynamical models.
E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky.
IEEE Transactions on Signal Processing, 59, 1569-1585, 2011.
Nonparametric combinatorial sequence models.
F. Wauthier, M. I. Jordan, and N. Jojic.
Journal of Computational Biology,
18, 1649-1660, 2011.
The SCADS Director: Scaling a distributed storage system under stringent
performance requirements.
B. Trushkowsky, P. Bodik, A. Fox, M. Franklin, M. I. Jordan, and D. Patterson.
In 9th USENIX Conference on File and Storage Technologies (FAST ’11),
San Jose, CA, 2011.
Learning dependency-based compositional semantics.
P. Liang, M. I. Jordan, and D. Klein.
The 49th Annual Meeting of the Association for Computational Linguistics (ACL),
[Long version].
Nonparametric Bayesian co-clustering ensembles.
P. Wang, K. B. Laskey, C. Domeniconi, and M. I. Jordan.
SIAM International Conference on Data Mining (SDM), Phoenix, AZ, 2011.
Dimensionality reduction for spectral clustering.
D. Niu, J. Dy, and M. I. Jordan.
In G. Gordon and D. Dunson (Eds.),
Proceedings of the Fourteenth Conference on Artificial Intelligence
and Statistics (AISTATS), Ft. Lauderdale, FL, 2011.
Nonparametric combinatorial sequence models.
F. Wauthier, M. I. Jordan, and N. Jojic.
15th Annual International Conference on Research in Computational Molecular Biology (RECOMB),
Vancouver, BC, 2011.
Message from the President: Visualizing Bayesians.
M. I. Jordan.
ISBA Bulletin, 18(3), 1-2, 2011.
Supervised hierarchical Pitman-Yor process for natural scene segmentation.
A. Shyr, T. Darrell, M. I. Jordan, and R. Urtasun.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Colorado Springs, CO, 2011.
A unified probabilistic model for global and local unsupervised feature selection.
Y. Guan, J. Dy, and M. I. Jordan.
In L. Getoor and T. Scheffer (Eds.),
Proceedings of the 28th International Conference on Machine
Learning (ICML), Bellevue, WA, 2011.
Message from the President: The era of Big Data.
M. I. Jordan.
ISBA Bulletin, 18(2), 1-3, 2011.
Managing data transfers in computer clusters with Orchestra.
M. Chowdhury, M. Zaharia, J. Ma, M. I. Jordan, and I. Stoica (2011).
ACM SIGCOMM, Toronto, Canada, 2011.
Visually relating gene expression and in vivo DNA binding data.
M.-Y. Huang, L. Mackey, S. Keranen, G. Weber, M. I. Jordan, D. Knowles,
M. Biggin, and B. Hamann.
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)
Atlanta, GA, 2011.
Message from the President: What are the open problems in Bayesian statistics?
M. I. Jordan.
ISBA Bulletin, 18(1), 1-4, 2011.
Ergodic subgradient descent.
J. C. Duchi, A. Agarwal, M. Johansson, and M. I. Jordan.
Forty-Ninth Annual Allerton Conference on Communication,
Control, and Computing, Urbana-Champaign, IL, 2011.
Variational inference over combinatorial spaces.
A. Bouchard-C�t� and M. I. Jordan.
In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.),
Advances in Neural Information Processing Systems (NIPS) 23, 2011.
[Supplementary information].
Random conic pursuit for semidefinite programming.
A. Kleiner, A. Rahimi, and M. I. Jordan.
In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.),
Advances in Neural Information Processing Systems (NIPS) 23, 2011.
[Supplementary information].
Heavy-tailed process priors for selective shrinkage.
F. L. Wauthier and M. I. Jordan.
In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.),
Advances in Neural Information Processing Systems (NIPS) 23, 2011.
Tree-structured stick breaking for hierarchical data.
R. Adams, Z. Ghahramani, and M. I. Jordan.
In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.),
Advances in Neural Information Processing Systems (NIPS) 23, 2011.
Cornea & refractive
Glaucoma, angle closure glaucoma
Myopia
Ocular inflammatory diseases
Retinal disease, diabetic retinopathy and AMD
Translational Clinical Research (TCR) Programme
Epidemiology
Ocular Imaging
Genetic Markers
Health and Service Science Research (HSSR)