Long Nguyen's articles by topics
 Statistical theory/ Learning theory/ Asymptotics
 Graphical models, hierarchical models, Bayesian nonparametrics
 Machine learning in networks and Database systems
 Reproducing Kernel Hilbert space and Gaussian process methods
 AI planning, search and constraint satisfaction
 Theses
 Tiếng Việt (Vietnamese)
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Convergence of de Finetti's mixing measure in latent structure models for observed exchangeable sequences.
Y. Wei and X. Nguyen.
arXiv:2004.05542.

On posterior contraction of parameters and interpretability in Bayesian mixture modeling.
A. Guha, N. Ho and X. Nguyen.
arXiv:1901.05078.

Singularity structures and impacts on parameter estimation in
finite mixtures of distributions.
N. Ho and X. Nguyen.
To appear,
SIAM Journal on Mathematics of Data Science.

Dirichlet simplex nest and geometric inference.
M. Yurochkin, A. Guha, Y. Sun and X. Nguyen.
Proceedings of the ICML, 2019.

Local inversionfree estimation of spatial Gaussian processes.
H. Keshavarz, X. Nguyen and C. Scott.
arXiv:1811.12602. To appear, Electronic Journal of Statistics.

Robust estimation of mixing measures in finite mixture models.
N. Ho, X. Nguyen and Y. Ritov.
arXiv:1709.08094. To appear, Bernoulli.

On the consistency of inversionfree parameter estimation for Gaussian random fields.
H. Keshavarz, C. Scott and X. Nguyen.
Journal of Multivariate Analysis, 150, 245266, 2016.

Convergence rates of parameter estimation for some weakly identifiable finite mixtures.
N. Ho and X. Nguyen. Annals of Statistics, 44(6), 27262755, 2016.

On strong identifiability and convergence rates of parameter estimation in finite mixtures.
N. Ho and X. Nguyen. Electronic Journal of Statistics, 10(1), 271307, 2016.

Identifiability and optimal rates of convergence for parameters of multiple
types in finite mixtures.
N. Ho and X. Nguyen.
Technical Report No. 536,
Dept of Statistics, Univ of Michigan, January 2015.

Optimal change point detection in Gaussian processes.
H. Keshavarz, C. Scott and X. Nguyen.
To appear, Journal of Statistical Planning and Inference.

Borrowing strength in hierarchical Bayes: posterior concentration of the Dirichlet base measure.
X. Nguyen. Bernoulli, 22(3), 15351571, 2016.
[Originally listed as Technical Report No. 532,
Dept of Statistics, Univ of Michigan, January 2013.]

Bayesian inference as iterated random functions with applications
to sequential inference in graphical models. A. A. Amini and X. Nguyen.
arXiv:1311.0072. Short version appears
Advances in Neural Processing Systems (NIPS) 26, 2013.

Posterior contraction of the population polytope in finite admixture
models.
X. Nguyen. Bernoulli, 21(1), 618646, 2015.
[Originally Technical Report No. 528,
Dept of Statistics, Univ of Michigan, May 2012.].

Sequential detection of multiple change points in
networks: A graphical model approach. A. A. Amini and X. Nguyen.
IEEE Transactions on Information Theory, 59(9), 58245841, 2013.

Convergence of latent mixing measures in finite and infinite
mixture models .
X. Nguyen. Annals of Statistics, 41(1), 370400, 2013.
[Corrections]

Estimating divergence functionals and the likelihood ratio by convex risk
minimization X. Nguyen, M.J. Wainwright and M.I. Jordan.
IEEE Transactions on Information Theory,
56(11), 58475861, 2010.
[ arXiv link ].

On surrogate loss functions and fdivergences.
X. Nguyen, M.J. Wainwright and M.I. Jordan.
Annals of Statistics, (37)2, 876904, 2009.

Support vector machines, data reduction and approximate kernel matrices.
X. Nguyen, L. Huang and A. Joseph. Proceedings of European
Conference on Machine Learning (ECML), Belgium, September, 2008.
(Previously listed as SAMSI Technical report No. 20083,
April 2008.)

On optimal quantization rules
in some problems in sequential decentralized detection.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
IEEE Transactions on Information Theory, 54(7), 32853295,
2008.

Estimating divergence functionals and the likelihood ratio
by penalized convex risk minimization.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
Advances in Neural Information Processing Systems 20 (NIPS), 2007
[ Correction ]
[ Full version ]

Nonparametric estimation of the likelihood ratio and divergence functionals.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
IEEE International Symposium on Information Theory (ISIT), Nice, France, July 2007.
[ Full version ]

Nonparametric decentralized detection using kernel methods.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
IEEE Transactions on Signal Processing, 53(11),
40534066, 2005.
"2007 IEEE Signal Processing Society's Young Author Best paper award".

Convergence of de Finetti's mixing measure in latent structure models for observed exchangeable sequences.
Y. Wei and X. Nguyen.
arXiv:2004.05542.

On posterior contraction of parameters and interpretability in Bayesian mixture modeling.
A. Guha, N. Ho and X. Nguyen.
arXiv:1901.05078.

Robust estimation of mixing measures in finite mixture models.
N. Ho, X. Nguyen and Y. Ritov.
arXiv:1709.08094. To appear, Bernoulli.

Scalable inference of topic evolution via models for latent geometric structures.
M. Yurochkin, Z. Fan, A. Guha, P. Koutris and X. Nguyen.
Advances in Neural Information Processing Systems (NeurIPS) 32, 2019.

Conic scanandcover algorithms for nonparametric topic modeling.
M. Yurochkin, A. Guha and X. Nguyen.
Advances in Neural Information Processing Systems (NIPS) 30, 2017.

Multiway interacting regression via factorization machines.
M. Yurochkin, X. Nguyen and N. Vasiloglou.
Advances in Neural Information Processing Systems (NIPS) 30, 2017.

Multilevel clustering via Wasserstein means.
N. Ho, X. Nguyen, M. Yurochkin, H. H. Bui, V. Huynh and D. Phung.
Proceedings of the 34th International Conference on
Machine Learning (ICML), Sydney, 2017.

Bayesian analysis of RNAseq data using
a family of negative binomial models.
L. Zhao, W. Wu, D. Feng, H. Jiang and X. Nguyen.
To appear, Bayesian Analysis.

Geometric
Dirichlet means algorithm for topic inference.
M. Yurochkin and X. Nguyen.
Advances in Neural Information Processing
Systems (NIPS) 29, 2016.

Singularity structures and impacts on parameter estimation in
finite mixtures of distributions.
N. Ho and X. Nguyen. arXiv:1609.02655.

Scalable nonparametric Bayesian multilevel clustering
V. Huynh, D. Phung, S. Venkatesh, X. Nguyen, M. Hoffman and H. H. Bui.
Proceedings of UAI, 2016.

Identifiability and optimal rates of convergence for parameters of multiple
types in finite mixtures.
N. Ho and X. Nguyen.
Technical Report No. 536,
Dept of Statistics, Univ of Michigan, January 2015.
This paper is superseded by this
ArXiv paper

Learning conditional latent structures from multiple data sources.
V. Huynh, D. Phung, X. Nguyen, S. Venkatesh and H. H. Bui.
Proceedings of the 19th PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD),
343354,
Ho Chi Minh City, May 2015.

Bayesian nonparametric multilevel clustering with grouplevel contexts.
V. Nguyen, D. Phung, X. Nguyen, S. Ventakesh and H. H. Bui.
Proceedings of the 31st International Conference on
Machine Learning (ICML), Beijing, June 2014.

Understanding the limiting factors of topic modeling via posterior contraction
analysis. J. Tang, Z. Meng, X. Nguyen, Q. Mei and M. Zhang.
Proceedings of the 31st International Conference on
Machine Learning (ICML), Beijing, June 2014. "Best paper award".

Borrowing strength in hierarchical Bayes: posterior concentration of the Dirichlet base measure.
X. Nguyen. Bernoulli, 22(3), 15351571, 2016.
[Originally listed as Technical Report No. 532,
Dept of Statistics, Univ of Michigan, January 2013.]

Bayesian inference as iterated random functions with applications
to sequential inference in graphical models. A. A. Amini and X. Nguyen.
arXiv:1311.0072. Short version appears in
Advances in Neural Processing Systems (NIPS) 26, 2013.

Posterior contraction of the population polytope in finite admixture
models.
X. Nguyen. Bernoulli, 21(1), 618646, 2015.
[Originally Technical Report No. 528,
Dept of Statistics, Univ of Michigan, May 2012.].

Bayesian nonparametric modeling for functional analysis of variance.
X. Nguyen and A. E. Gelfand. Annals of the Institute of
Statistical Mathematics, 66(3), 496526, 2014.

Convergence of latent mixing measures in finite and infinite
mixture models .
X. Nguyen. Annals of Statistics, 41(1), 370400, 2013.
[Corrections]

Sequential detection of multiple change points in
networks: A graphical model approach. A. A. Amini and X. Nguyen.
IEEE Transactions on Information Theory, 59(9), 58245841, 2013.

Messagepassing sequential detection of multiple change points in
networks.
X. Nguyen, A. A. Amini and R. Rajagopal. IEEE International Symposium
on Information Theory (ISIT), Boston, July 2012.

The Dirichlet labeling process for clustering functional data.
X. Nguyen and A. E. Gelfand.
Statistica Sinica 21(3), 12491289, 2011.

Inferential ecosystem models, from network data to prediction.
J.S. Clark, P. Agarwal, D.M. Bell, P. Flikkema, A. Gelfand, X. Nguyen, E. Ward, and J. Yang.
Ecological Applications, 21(5), 1523–1536, 2011.

Inference of global clusters from locally distributed data.
X. Nguyen.
Bayesian Analysis, 5(4), 817846, 2010.
 Preliminary version appeared as
Graphically dependent and spatially varying Dirichlet process mixtures.
X. Nguyen.
Technical Report 504, Dept. of Statistics, University of Michigan, January 2010.
[ arXiv link ].

On the concentration of expectation and approximate inference in
layered networks.
X. Nguyen and M. I. Jordan.
Advances in Neural Information Processing Systems 16 (NIPS), 2003.
[ citations ]
 Fulllength version
[ ps ].

Local inversionfree estimation of spatial Gaussian processes.
H. Keshavarz, X. Nguyen and C. Scott.
arXiv:1811.12602. To appear, Electronic Journal of Statistics.

On the consistency of inversionfree parameter estimation for Gaussian random fields.
H. Keshavarz, C. Scott and X. Nguyen.
Journal of Multivariate Analysis, 150, 245266, 2016.

Optimal change point detection in Gaussian processes.
H. Keshavarz, C. Scott and X. Nguyen.
To appear, Journal of Statistical Planning and Inference.

Bayesian nonparametric modeling for functional analysis of variance.
X. Nguyen and A. E. Gelfand. Annals of the Institute of
Statistical Mathematics, 66(3), 496526, 2014.

The Dirichlet labeling process for clustering functional data.
X. Nguyen and A. E. Gelfand.
Statistica Sinica 21(3), 12491289, 2011.

Estimating divergence functionals and the likelihood ratio by convex risk
minimization X. Nguyen, M.J. Wainwright and M.I. Jordan.
IEEE Transactions on Information Theory,
56(11), 58475861, 2010.
[ arXiv link ].

Inference of global clusters from locally distributed data.
X. Nguyen.
Bayesian Analysis, 5(4), 817846, 2010.
 Preliminary version appeared as
Graphically dependent and spatially varying Dirichlet process mixtures.
X. Nguyen.
Technical Report 504, Dept. of Statistics, University of Michigan, January 2010.
[ arXiv link ].

Support vector machines, data reduction and approximate kernel matrices.
X. Nguyen, L. Huang and A. Joseph. Proceedings of European
Conference on Machine Learning (ECML), Belgium, September, 2008.
(Previously listed as SAMSI Technical report No. 20083,
April 2008.)

Estimating divergence functionals and the likelihood ratio
by penalized convex risk minimization.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
Advances in Neural Information Processing Systems 20 (NIPS), 2007
[ Correction ]
[ Full version ]

Nonparametric estimation of the likelihood ratio and divergence functionals.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
IEEE International Symposium on Information Theory (ISIT), Nice, France, July 2007.
[ Full version ]

A kernelbased learning approach to ad hoc sensor network localization.
X. Nguyen, M. I. Jordan and B. Sinopoli.
ACM Transactions on Sensor Networks, 1, 134152, 2005.
Also listed as technical report CSD041319, Computer Science Division,
University of California, Berkeley, April 2004.
[ Corrections].
 Conference version:
A kernelbased learning approach to ad hoc sensor network localization.
X. Nguyen, M. I. Jordan and B. Sinopoli. In
Proc. AAAI2004 Workshop on Sensor Networks , San Jose, CA, July 2004.

Nonparametric decentralized detection using kernel methods.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
IEEE Transactions on Signal Processing, 53(11),
40534066, 2005.
"2007 IEEE Signal Processing Society's Young Author Best paper award".
 Conference version:
Decentralized detection and classification using kernel methods.
X. Nguyen, M. J. Wainwright and M. I. Jordan. In
Proceedings of the 21st International Conference on Machine Learning
(ICML), Banff, Canada, 2004.
"Best Paper Award".
 Tech report version:
Decentralized Detection and Classification using Kernel Methods.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
Technical Report 658, Department of Statistics, University of California,
Berkeley, April 2004.
[ pdf ].

Rkmeans: Fast Clustering for Relational Data.
R. Curtin, B. Moseley, H. Ngo, X. Nguyen, D. Olteanu and M. Schleich.
To appear,
Proceedings of the 23rd International Conference on
Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, 2020.

On functional aggregate queries with additive inequalities.
M. Abo Khamis, R. Curtin, B. Moseley, H. Ngo, X. Nguyen, D. Olteanu and M. Schleich.
arXiv:1812.09526.
To appear, Proceedings of the ACM SIGMOD/PODS conference, Amsterdam, the
Netherlands, 2019.

A layered aggregate engine for analytics workloads .
M. Schleich, D. Olteanu, M. Abo Khamis, H. Ngo and X. Nguyen.
To appear, Proceedings of the ACM SIGMOD/PODS conference, Amsterdam, the
Netherlands, 2019.

Learning models over relational data using sparse tensors and functional dependencies.
M. Abo Khamis, H. Q. Ngo, X. Nguyen, D. Olteanu and M. Schleich.
To appear, ACM Transactions on Database Systems, 2020.

Indatabase learning with sparse tensors
M. Abo Khamis, H. Q. Ngo, X. Nguyen, D. Olteanu and M. Schleich.
arXiv:1703.04780.
To appear, Proceedings of the 37th ACM SIGMODSIGACTSIGAI Symposium on Principles of Database Systems
(PODS), 2018.

Optimal change point detection in Gaussian processes.
H. Keshavarz, C. Scott and X. Nguyen.
To appear, Journal of Statistical Planning and Inference.

Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines.
V. M. Janakiraman, X. Nguyen, and D. Assanis.
Neurocomputing, 177, 304316, 2016.

Parallel feature selection inspired by group testing.
Y. Zhou, C. Zhang, U. Porwal, H. Q. Ngo, X. Nguyen, C. Ré,
and V. Govindaraju.
Advances in Neural Information Processing
Systems (NIPS) 27, 2014.

Bayesian inference as iterated random functions with applications
to sequential inference in graphical models. A. A. Amini and X. Nguyen.
arXiv:1311.0072. Short version appears in
Advances in Neural Processing Systems (NIPS) 26, 2013.

Identification of the dynamic operating envelope of HCCI engines using class imbalance learning
V. M. Janakiraman, X. Nguyen, J. Sterniak and D. Assanis.
IEEE Transactions on Neural Networks and Learning Systems,
26(1), 98112, 2015.

Sequential detection of multiple change points in
networks: A graphical model approach. A. A. Amini and X. Nguyen.
IEEE Transactions on Information Theory, 59(9), 58245841, 2013.

A Lyapunov based stable online learning algorithm for nonlinear dynamical systems using extreme learning machines.
V. M. Janakiraman, X. Nguyen, and D. Assanis.
Proceedings of the International Joint Conference on Neural Networks (IJCNN) ,
Dallas, August 2013.

Messagepassing sequential detection of multiple change points in
networks.
X. Nguyen, A. A. Amini and R. Rajagopal. IEEE International Symposium
on Information Theory (ISIT), Boston, July 2012.

Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis.
V. M. Janakiraman, X. Nguyen and D. Assanis.
Journal of Applied Soft Computing, 13(5), 23752389, 2013.

Wireless sensor networks: Statistical issues and challenges.
S. N. Lahiri, X. Nguyen, J. Yang, Z. Zhu, and P. Banerjee.
Journal of the Indian Statistical Association, 50(12), 151191, 2012.

Inferential ecosystem models, from network data to prediction.
J. S. Clark, P. Agarwal, D. M. Bell, P. Flikkema, A. Gelfand, X. Nguyen, E. Ward, and J. Yang.
Ecological Applications, 21(5), 1523–1536, 2011.

Simultaneous sequential detection of multiple interacting faults.
R. Rajagopal, X. Nguyen, S.C. Ergen and P. Varaiya.
http://arxiv.org/abs/1012.1258

Localization problem in sensor networks: The machine learning approach.
Duc A. Tran, X. Nguyen, and T. Nguyen.
Book chapter in G. Mao and B. Fidan (Eds), Localization
Algorithms and Strategies for Wireless Sensor Networks:
Monitoring and Surveillance Techniques for Target Tracking,
IGI Global Publisher, 2009.

Distributed online simultaneous
fault detection for multiple sensors.
R. Rajagopal, X. Nguyen, S. Ergen and P. Varaiya.
Proceedings of the 7th International Conference on
Information Processing in Sensor Networks (IPSN) , St. Louis, MO, April, 2008.

On optimal quantization rules
in some problems in sequential decentralized detection.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
IEEE Transactions on Information Theory, 54(7), 32853295,
2008.
 Technical report version (outdated):
On optimal quantization rules in some sequential decision problems.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
Technical Report 708, Department of Statistics, UC Berkeley, June 2006.
 Conference version:
On optimal quantization rules in sequential decision problems.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
IEEE International Symposium on Information Theory (ISIT), Seattle,
CA 2006.

CommunicationEfficient Online Detection of NetworkWide Anomalies.
L. Huang, X. Nguyen, M. Garofalakis, J. Hellerstein, A. Joseph, M.I. Jordan and
N. Taft. 26th Annual IEEE INFOCOM, Anchorage, Alaska, May 2007.

Innetwork PCA and anomaly detection.
L. Huang, X. Nguyen, M. Garofalakis, M. I. Jordan, A. D. Joseph and N. Taft.
Advances in Neural Information Processing Systems 19 (NIPS), 2006.
 Tech report version:
Innetwork PCA and network anomaly detection,
Technical Report UCB/EECS200699,
Department of EECS, UC Berkeley, July 2006.
[ pdf ]

On divergences, surrogate loss functions and decentralized
detection. X. Nguyen, M. J. Wainwright and M. I. Jordan.
Technical Report 695, Department of Statistics, UC Berkeley, October 2005.
(An abridged version appears in Annals of Statistics, 2009).
Conference versions:

Divergences, surrogate loss functions and experimental design.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
Advances in Neural Information Processing Systems 18 (NIPS), 2005.
[ pdf ]

On information divergence measures, surrogate loss functions and
decentralized hypothesis testing.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
43rd Annual Allerton Conference on Communication, Control
and Computing, Allerton, IL, September 2005.
[ pdf ]

A kernelbased learning approach to ad hoc sensor network localization.
X. Nguyen, M. I. Jordan and B. Sinopoli.
ACM Transactions on Sensor Networks, 1, 134152, 2005.
Also listed as technical report CSD041319, Computer Science Division,
University of California, Berkeley, April 2004.
[ Corrections].
 Conference version:
A kernelbased learning approach to ad hoc sensor network localization.
X. Nguyen, M. I. Jordan and B. Sinopoli. In
Proc. AAAI2004 Workshop on Sensor Networks , San Jose, CA, July 2004.

Nonparametric decentralized detection using kernel methods.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
IEEE Transactions on Signal Processing, 53(11),
40534066, 2005.
"2007 IEEE Signal Processing Society's Young Author Best paper award".
 Conference version:
Decentralized detection and classification using kernel methods.
X. Nguyen, M. J. Wainwright and M. I. Jordan. In
Proceedings of the 21st International Conference on Machine Learning
(ICML), Banff, Canada, 2004.
"Best Paper Award".
 Tech report version:
Decentralized Detection and Classification using Kernel Methods.
X. Nguyen, M. J. Wainwright and M. I. Jordan.
Technical Report 658, Department of Statistics, University of California,
Berkeley, April 2004.
[ pdf ].

A new localization algorithm for sensor networks using RF signal strength.
X. Nguyen and T. Rattenbury, CS 252 report, Spring 2003.

Planning Graph as the Basis for deriving Heuristics for Plan Synthesis
by State Space and CSP Search.
X. Nguyen, S. Kambhampati & R. SanchezNigenda.
Artificial Intelligence Journal, 135(12), 73124, 2002.

Reviving Partial Order Planning.
X. Nguyen and S. Kambhampati,
In Proceedings of the 17th International Joint Conference on
Artificial Intelligence (IJCAI), Seattle, CA, 459466, 2001.

AltAlt: Combining the advantages of Graphplan and heuristic state search.
R. SanchezNigenda, X. Nguyen and S. Kambhampati,
Proceedings of the 3rd International Conference on Knowledgebased
Systems (KBCS), Bombay, India, 2000.
[ pdf ].
 Shorter version:
AltAlt: Combining Graphplan and heuristic state search.
Artificial Intelligence Magazine 22(3), 8890, 2001.

Extracting Effective and Admissible State Space Heuristics from
the Planning Graph.
X. Nguyen and S. Kambhampati,
In Proceedings of the 17th National Conference on Artificial
Intelligence (AAAI), Austin, TX, 798805, 2000.

Learning in decentralized systems: A nonparametric
approach. XuanLong Nguyen, PhD Thesis,
University of California, Berkeley, 2007.
[ pdf ] (203 pages).
Advisors: Michael Jordan
and Martin Wainwright .
"2007 Leon O. Chua Award" from UC Berkeley for doctoral work.

Heuristic Search Control for Plan Synthesis Algorithms and Dynamic Constraint
Satisfaction Problems (90 pages)
[ pdf ].
X. Nguyen, Master Thesis, Arizona State University, Aug 2001.
Advisor: Subbarao Kambhampati.

Khoa học dữ liệu trong cuộc sống hiện đại: Phỏng vấn PGS
Nguyễn Xuân Long. Vietnam Journal of Science, tháng 7, 2017.

Tìm chân lý từ dữ liệu thô: suy diễn chủ đề và hình học.
Nguyễn Xuân Long, Pi Magazine , số tháng 5, 2017.

Trí tuệ nhân tạo và những vị chúa tể mới.
Ngô Quang Hưng và Nguyễn Xuân Long, Tạp chí Tia Sáng, số kỷ niệm 20 năm, 2011.
 Lexicon in probability, statistics and machine learning: Xác suất, thống kê và học máy
(under construction).
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