XuanLong Nguyen

Affiliations:
  • University of Michigan, Department of Statistics


According to our database1, XuanLong Nguyen authored at least 65 papers between 2000 and 2023.

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Bibliography

2023
Interpolation for Robust Learning: Data Augmentation on Geodesics.
CoRR, 2023

Scalable nonparametric Bayesian learning for dynamic velocity fields.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics.
Proceedings of the International Conference on Machine Learning, 2023

On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances.
Proceedings of the International Conference on Machine Learning, 2023

2022
Beyond black box densities: Parameter learning for the deviated components.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression.
Proceedings of the Conference on Health, Inference, and Learning, 2022

2021
On efficient multilevel Clustering via Wasserstein distances.
J. Mach. Learn. Res., 2021

Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields.
CoRR, 2021

Functional Optimal Transport: Mapping Estimation and Domain Adaptation for Functional data.
CoRR, 2021

2020
Learning Models over Relational Data Using Sparse Tensors and Functional Dependencies.
ACM Trans. Database Syst., 2020

Functional Aggregate Queries with Additive Inequalities.
ACM Trans. Database Syst., 2020

Robust Unsupervised Learning of Temporal Dynamic Interactions.
CoRR, 2020

Rk-means: Fast Clustering for Relational Data.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Singularity Structures and Impacts on Parameter Estimation in Finite Mixtures of Distributions.
SIAM J. Math. Data Sci., 2019

Learning Models over Relational Data: A Brief Tutorial.
Proceedings of the Scalable Uncertainty Management - 13th International Conference, 2019

A Layered Aggregate Engine for Analytics Workloads.
Proceedings of the 2019 International Conference on Management of Data, 2019

Scalable inference of topic evolution via models for latent geometric structures.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Dirichlet Simplex Nest and Geometric Inference.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Streaming dynamic and distributed inference of latent geometric structures.
CoRR, 2018

AC/DC: In-Database Learning Thunderstruck.
Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, 2018

In-Database Learning with Sparse Tensors.
Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2018

2017
Multi-way Interacting Regression via Factorization Machines.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Conic Scan-and-Cover algorithms for nonparametric topic modeling.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Multilevel Clustering via Wasserstein Means.
Proceedings of the 34th International Conference on Machine Learning, 2017

In-Database Factorized Learning.
Proceedings of the 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web, 2017

2016
On the consistency of inversion-free parameter estimation for Gaussian random fields.
J. Multivar. Anal., 2016

Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines.
Neurocomputing, 2016

An ELM based predictive control method for HCCI engines.
Eng. Appl. Artif. Intell., 2016

Scalable Nonparametric Bayesian Multilevel Clustering.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Geometric Dirichlet Means Algorithm for topic inference.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Identification of the Dynamic Operating Envelope of HCCI Engines Using Class Imbalance Learning.
IEEE Trans. Neural Networks Learn. Syst., 2015

Optimal change point detection in Gaussian processes.
CoRR, 2015

Stochastic Gradient Based Extreme Learning Machines For Online Learning of Advanced Combustion Engines.
CoRR, 2015

Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines.
CoRR, 2015

Learning Conditional Latent Structures from Multiple Data Sources.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2015

2014
Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis.
Proceedings of the 31th International Conference on Machine Learning, 2014

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Sequential Detection of Multiple Change Points in Networks: A Graphical Model Approach.
IEEE Trans. Inf. Theory, 2013

Borrowing strength in hierarchical Bayes: convergence of the Dirichlet base measure
CoRR, 2013

Modeling The Stable Operating Envelope For Partially Stable Combustion Engines Using Class Imbalance Learning.
CoRR, 2013

Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis.
Appl. Soft Comput., 2013

Bayesian inference as iterated random functions with applications to sequential inference in graphical models.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012
Posterior contraction of the population polytope in finite admixture models
CoRR, 2012

Message-passing sequential detection of multiple change points in networks.
Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

2010
Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization.
IEEE Trans. Inf. Theory, 2010

Simultaneous Sequential Detection of Multiple Interacting Faults
CoRR, 2010

Inference of global clusters from locally distributed data.
CoRR, 2010

2008
On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection.
IEEE Trans. Inf. Theory, 2008

Support Vector Machines, Data Reduction, and Approximate Kernel Matrices.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Distributed Online Simultaneous Fault Detection for Multiple Sensors.
Proceedings of the 7th International Conference on Information Processing in Sensor Networks, 2008

2007
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Nonparametric estimation of the likelihood ratio and divergence functionals.
Proceedings of the IEEE International Symposium on Information Theory, 2007

Communication-Efficient Online Detection of Network-Wide Anomalies.
Proceedings of the INFOCOM 2007. 26th IEEE International Conference on Computer Communications, 2007

2006
On optimal quantization rules for some sequential decision problems
CoRR, 2006

In-Network PCA and Anomaly Detection.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

On optimal quantization rules for sequential decision problems.
Proceedings of the Proceedings 2006 IEEE International Symposium on Information Theory, 2006

2005
Nonparametric decentralized detection using kernel methods.
IEEE Trans. Signal Process., 2005

A kernel-based learning approach to ad hoc sensor network localization.
ACM Trans. Sens. Networks, 2005

On divergences, surrogate loss functions, and decentralized detection
CoRR, 2005

Divergences, surrogate loss functions and experimental design.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004
Decentralized detection and classification using kernel methods.
Proceedings of the Machine Learning, 2004

2003
On the Concentration of Expectation and Approximate Inference in Layered Networks.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

2002
Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search.
Artif. Intell., 2002

2001
Reviving Partial Order Planning.
Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 2001

2000
Extracting Effective and Admissible State Space Heuristics from the Planning Graph.
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on on Innovative Applications of Artificial Intelligence, July 30, 2000


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