Search Results for author: XuanLong Nguyen

Found 26 papers, 7 papers with code

Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields

no code implementations15 Feb 2021 Sunrit Chakraborty, Aritra Guha, Rayleigh Lei, XuanLong Nguyen

Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to navigate in complex traffic scenarios.

Autonomous Vehicles

Functional optimal transport: map estimation and domain adaptation for functional data

1 code implementation7 Feb 2021 Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao

We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in terms of an (infinite-dimensional) Hilbert-Schmidt operator mapping a Hilbert space of functions to another.

Domain Adaptation Transfer Learning

Robust Unsupervised Learning of Temporal Dynamic Interactions

no code implementations18 Jun 2020 Aritra Guha, Rayleigh Lei, Jiacheng Zhu, XuanLong Nguyen, Ding Zhao

These distance metrics can serve as an objective for assessing the stability of an interaction learning algorithm.

Representation Learning

Rk-means: Fast Clustering for Relational Data

no code implementations11 Oct 2019 Ryan Curtin, Ben Moseley, Hung Q. Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich

When the data matrix needs to be obtained from a relational database via a feature extraction query, the computation cost can be prohibitive, as the data matrix may be (much) larger than the total input relation size.

On Efficient Multilevel Clustering via Wasserstein Distances

1 code implementation19 Sep 2019 Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, and Dinh Phung

We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data.

Dirichlet Simplex Nest and Geometric Inference

1 code implementation27 May 2019 Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen

We propose Dirichlet Simplex Nest, a class of probabilistic models suitable for a variety of data types, and develop fast and provably accurate inference algorithms by accounting for the model's convex geometry and low dimensional simplicial structure.

Scalable inference of topic evolution via models for latent geometric structures

1 code implementation NeurIPS 2019 Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen

We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference.

UPS: optimizing Undirected Positive Sparse graph for neural graph filtering

no code implementations ICLR 2018 Mikhail Yurochkin, Dung Thai, Hung Hai Bui, XuanLong Nguyen

In this work we propose a novel approach for learning graph representation of the data using gradients obtained via backpropagation.

Multi-way Interacting Regression via Factorization Machines

1 code implementation NeurIPS 2017 Mikhail Yurochkin, XuanLong Nguyen, Nikolaos Vasiloglou

We propose a Bayesian regression method that accounts for multi-way interactions of arbitrary orders among the predictor variables.

Multilevel Clustering via Wasserstein Means

1 code implementation ICML 2017 Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Phung

We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data.

Geometric Dirichlet Means algorithm for topic inference

no code implementations NeurIPS 2016 Mikhail Yurochkin, XuanLong Nguyen

We propose a geometric algorithm for topic learning and inference that is built on the convex geometry of topics arising from the Latent Dirichlet Allocation (LDA) model and its nonparametric extensions.

Variational Inference

Singularity structures and impacts on parameter estimation in finite mixtures of distributions

no code implementations9 Sep 2016 Nhat Ho, XuanLong Nguyen

Our study makes explicit the deep links between model singularities, parameter estimation convergence rates and minimax lower bounds, and the algebraic geometry of the parameter space for mixtures of continuous distributions.

Optimal change point detection in Gaussian processes

no code implementations3 Jun 2015 Hossein Keshavarz, Clayton Scott, XuanLong Nguyen

By contrast, the standard CUSUM method, which does not account for the covariance structure, is shown to be asymptotically optimal only in the increasing domain.

Change Point Detection Gaussian Processes +2

Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines

no code implementations16 Jan 2015 Vijay Manikandan Janakiraman, XuanLong Nguyen, Dennis Assanis

Using the ELM engine models, an MPC based control algorithm with a simplified quadratic program update is derived for real time implementation.

Stochastic Gradient Based Extreme Learning Machines For Online Learning of Advanced Combustion Engines

no code implementations16 Jan 2015 Vijay Manikandan Janakiraman, XuanLong Nguyen, Dennis Assanis

The algorithm is applied to two case studies: An online regression learning for system identification of a Homogeneous Charge Compression Ignition (HCCI) Engine and an online classification learning (with class imbalance) for identifying the dynamic operating envelope of the HCCI Engine.

Identifiability and optimal rates of convergence for parameters of multiple types in finite mixtures

no code implementations11 Jan 2015 Nhat Ho, XuanLong Nguyen

This paper studies identifiability and convergence behaviors for parameters of multiple types in finite mixtures, and the effects of model fitting with extra mixing components.

Parallel Feature Selection Inspired by Group Testing

no code implementations NeurIPS 2014 Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q. Ngo, XuanLong Nguyen, Christopher Ré, Venu Govindaraju

Superior performance of our method is demonstrated on a challenging relation extraction task from a very large data set that have both redundant features and sample size in the order of millions.

Feature Selection General Classification +1

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts

no code implementations9 Jan 2014 Vu Nguyen, Dinh Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Hai Bui

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters.

Bayesian inference as iterated random functions with applications to sequential inference in graphical models

no code implementations NeurIPS 2013 Arash A. Amini, XuanLong Nguyen

We propose a general formalism of iterated random functions with semigroup property, under which exact and approximate Bayesian posterior updates can be viewed as specific instances.

Bayesian Inference Change Point Detection

Modeling The Stable Operating Envelope For Partially Stable Combustion Engines Using Class Imbalance Learning

no code implementations24 Jun 2013 Vijay Manikandan Janakiraman, XuanLong Nguyen, Jeff Sterniak, Dennis Assanis

In this paper, a machine learning based approach is employed to identify the stable operating boundary of HCCI combustion directly from experimental data.

Borrowing strengh in hierarchical Bayes: Posterior concentration of the Dirichlet base measure

no code implementations4 Jan 2013 XuanLong Nguyen

This paper studies posterior concentration behavior of the base probability measure of a Dirichlet measure, given observations associated with the sampled Dirichlet processes, as the number of observations tends to infinity.

Posterior contraction of the population polytope in finite admixture models

no code implementations1 Jun 2012 XuanLong Nguyen

We study the posterior contraction behavior of the latent population structure that arises in admixture models as the amount of data increases.

Topic Models

Convergence of latent mixing measures in finite and infinite mixture models

no code implementations15 Sep 2011 XuanLong Nguyen

This paper studies convergence behavior of latent mixing measures that arise in finite and infinite mixture models, using transportation distances (i. e., Wasserstein metrics).

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