Long Nguyen's articles by topics

1. Theory: {statistical theory, statistical learning theory, asymptotic analysis}
2. Optimal transport and statistical inference
3. Stochastic modeling: {graphical models, hierarchical Bayes, Bayesian nonparametrics}
4. Algorithms and methods: machine learning in {networks, databases, systems}
5. Functional and spatiotemporal data analysis: {reproducing kernel Hilbert space, Gaussian process methods}
6. Classical AI: {automated planning, search, constraint satisfaction}
7. Tiếng Việt (Vietnamese)

Statistical theory, statistical learning theory, asymptotic analysis

• Strong identifiability and parameter learning in regression with heterogeneous response. D. Do, L. Do, and X. Nguyen. arXiv:2212.04091.

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

• Beyond black box densities: parameter learning for the deviated components. D. Do, N. Ho and X. Nguyen. arXiv:2202.02651. Advances in Neural Information Processing Systems (NeurIPS) 35, 2022.

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

• 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 inversion-free 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 inversion-free parameter estimation for Gaussian random fields. H. Keshavarz, C. Scott and X. Nguyen. Journal of Multivariate Analysis, 150, 245--266, 2016.

• Convergence rates of parameter estimation for some weakly identifiable finite mixtures. N. Ho and X. Nguyen. Annals of Statistics, 44(6), 2726--2755, 2016.

• On strong identifiability and convergence rates of parameter estimation in finite mixtures. N. Ho and X. Nguyen. Electronic Journal of Statistics, 10(1), 271--307, 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), 1535--1571, 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), 618--646, 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), 5824--5841, 2013.

• Convergence of latent mixing measures in finite and infinite mixture models . X. Nguyen. Annals of Statistics, 41(1), 370--400, 2013. [arXiv:1109.3250] [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), 5847--5861, 2010. [ arXiv link ].

• On surrogate loss functions and f-divergences. X. Nguyen, M. J. Wainwright and M. I. Jordan. Annals of Statistics, (37)2, 876--904, 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. 2008-3, 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), 3285--3295, 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), 4053--4066, 2005. "2007 IEEE Signal Processing Society's Young Author Best paper award".

Optimal transport and statistical inference

• Strong identifiability and parameter learning in regression with heterogeneous response. D. Do, L. Do, and X. Nguyen. arXiv:2212.04091.

• Beyond black box densities: parameter learning for the deviated components. D. Do, N. Ho and X. Nguyen. arXiv:2202.02651. Advances in Neural Information Processing Systems (NeurIPS) 35, 2022.

• PhysioMTL: Personalizing physiological patterns using optimal transport multi-task regression. J. Zhu, G. Darnell, A. Kumar, D. Zhao, B. Li, X. Nguyen, S. Y. Ren. arXiv:2203.12595. Proceedings of Conference on Health, Inference, and Learning (CHIL), 2022.

• Functional optimal transport: map estimation and domain adaptation for functional data. J. Zhu, A. Guha, D. Do, M. Xu, X. Nguyen and D. Zhao. arXiv:2102.03895.

• Rk-means: Fast clustering for relational data. R. Curtin, B. Moseley, H. Ngo, X. Nguyen, D. Olteanu and M. Schleich. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, 2020.

• Singularity structures and impacts on parameter estimation in finite mixtures of distributions. N. Ho and X. Nguyen. SIAM Journal on Mathematics of Data Science, 2019.

• 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.

• Borrowing strength in hierarchical Bayes: posterior concentration of the Dirichlet base measure. X. Nguyen. Bernoulli, 22(3), 1535--1571, 2016. [Originally listed as Technical Report No. 532, Dept of Statistics, Univ of Michigan, January 2013.]

• Convergence of latent mixing measures in finite and infinite mixture models . X. Nguyen. Annals of Statistics, 41(1), 370--400, 2013. [arXiv:1109.3250] [Corrections]

Graphical models, hierarchical Bayes, Bayesian nonparametrics

• Modeling random directions in 2D simplex data. R. Lei and X. Nguyen. arXiv:2103.12214.

• Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields. S. Chakraborty, A. Guha, R. Lei and X. Nguyen. arXiv:2102.07695.

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

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

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

• Multi-way 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 RNA-seq 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 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 343--354, Ho Chi Minh City, May 2015.

• Bayesian nonparametric multilevel clustering with group-level 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), 1535--1571, 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), 618--646, 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), 496--526, 2014.

• Convergence of latent mixing measures in finite and infinite mixture models . X. Nguyen. Annals of Statistics, 41(1), 370--400, 2013. [arXiv:1109.3250] [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), 5824--5841, 2013.

• Message-passing 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), 1249-1289, 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), 817--846, 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 ]
  • Full-length version [ ps ].

Reproducing kernel Hilbert space and Gaussian Process Methods

• Functional optimal transport: map estimation and domain adaptation for functional data. J. Zhu, A. Guha, D. Do, M. Xu, X. Nguyen and D. Zhao. arXiv:2102.03895.

• Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields. S. Chakraborty, A. Guha, R. Lei and X. Nguyen. arXiv:2102.07695.

• Local inversion-free 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 inversion-free parameter estimation for Gaussian random fields. H. Keshavarz, C. Scott and X. Nguyen. Journal of Multivariate Analysis, 150, 245--266, 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), 496--526, 2014.

• The Dirichlet labeling process for clustering functional data. X. Nguyen and A. E. Gelfand. Statistica Sinica 21(3), 1249-1289, 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), 5847--5861, 2010. [ arXiv link ].

• Inference of global clusters from locally distributed data. X. Nguyen. Bayesian Analysis, 5(4), 817--846, 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. 2008-3, 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 kernel-based learning approach to ad hoc sensor network localization. X. Nguyen, M. I. Jordan and B. Sinopoli. ACM Transactions on Sensor Networks, 1, 134--152, 2005. Also listed as technical report CSD-04-1319, Computer Science Division, University of California, Berkeley, April 2004. [ Corrections].
  • Conference version: A kernel-based learning approach to ad hoc sensor network localization. X. Nguyen, M. I. Jordan and B. Sinopoli. In Proc. AAAI-2004 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), 4053--4066, 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 ].

Machine learning in networks, databases and systems

• PhysioMTL: Personalizing physiological patterns using optimal transport multi-task regression. J. Zhu, G. Darnell, A. Kumar, D. Zhao, B. Li, X. Nguyen, S. Y. Ren. arXiv:2203.12595. Proceedings of Conference on Health, Inference, and Learning (CHIL), 2022.

• Functional optimal transport: map estimation and domain adaptation for functional data. J. Zhu, A. Guha, D. Do, M. Xu, X. Nguyen and D. Zhao. arXiv:2102.03895.

• Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields. S. Chakraborty, A. Guha, R. Lei and X. Nguyen. arXiv:2102.07695.

• Robust unsupervised learning of temporal dynamic interactions. A. Guha, R. Lei, J. Zhu, X. Nguyen and D. Zhao. arXiv:2006.10241. To appear, Transportation Research Part C: Emerging Technologies.

• Rk-means: Fast clustering for relational data. R. Curtin, B. Moseley, H. Ngo, X. Nguyen, D. Olteanu and M. Schleich. 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, ACM Transactions on Database Systems, 2020. A preliminary version appeared in 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.

• In-database learning with sparse tensors M. Abo Khamis, H. Q. Ngo, X. Nguyen, D. Olteanu and M. Schleich. arXiv:1703.04780. Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI 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, 304--316, 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), 98--112, 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), 5824--5841, 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.

• Message-passing 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), 2375--2389, 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(1-2), 151--191, 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), 3285--3295, 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.

• Communication-Efficient Online Detection of Network-Wide 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.

• In-network 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: In-network PCA and network anomaly detection, Technical Report UCB/EECS-2006-99, 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 kernel-based learning approach to ad hoc sensor network localization. X. Nguyen, M. I. Jordan and B. Sinopoli. ACM Transactions on Sensor Networks, 1, 134--152, 2005. Also listed as technical report CSD-04-1319, Computer Science Division, University of California, Berkeley, April 2004. [ Corrections].
  • Conference version: A kernel-based learning approach to ad hoc sensor network localization. X. Nguyen, M. I. Jordan and B. Sinopoli. In Proc. AAAI-2004 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), 4053--4066, 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.

AI Planning, Search and Constraint Satisfaction

• Planning Graph as the Basis for deriving Heuristics for Plan Synthesis by State Space and CSP Search. X. Nguyen, S. Kambhampati & R. Sanchez-Nigenda. Artificial Intelligence Journal, 135(1-2), 73--124, 2002.
  • AltAlt planner [ homepage ].

• Reviving Partial Order Planning. X. Nguyen and S. Kambhampati, In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI), Seattle, CA, 459--466, 2001.
  • RePOP planner [ homepage ], its precursor [ UCPOP ] and its "successor" [ VHPOP ].

• AltAlt: Combining the advantages of Graphplan and heuristic state search. R. Sanchez-Nigenda, X. Nguyen and S. Kambhampati, Proceedings of the 3rd International Conference on Knowledge-based Systems (KBCS), Bombay, India, 2000. [ pdf ].
  • Shorter version: AltAlt: Combining Graphplan and heuristic state search. Artificial Intelligence Magazine 22(3), 88--90, 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, 798--805, 2000.

Theses

• 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.

Tiếng Việt

• 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).