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04/20/2023
Minimum Φ-distance estimators for finite mixing measures
Finite mixture models have long been used across a variety of fields in ...
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02/04/2023
Interpolation for Robust Learning: Data Augmentation on Geodesics
We propose to study and promote the robustness of a model as per its per...
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01/27/2023
On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances
Dirichlet Process mixture models (DPMM) in combination with Gaussian ker...
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12/08/2022
Strong identifiability and parameter learning in regression with heterogeneous response
Mixtures of regression are a powerful class of models for regression lea...
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03/19/2022
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
Heart rate variability (HRV) is a practical and noninvasive measure of a...
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02/05/2022
Beyond Black Box Densities: Parameter Learning for the Deviated Components
As we collect additional samples from a data population for which a know...
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02/15/2021
Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields
Analysis of heterogeneous patterns in complex spatio-temporal data finds...
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02/07/2021
Functional Optimal Transport: Mapping Estimation and Domain Adaptation for Functional data
Optimal transport (OT) has generated much recent interest by its capabil...
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06/18/2020
Robust Unsupervised Learning of Temporal Dynamic Interactions
Robust representation learning of temporal dynamic interactions is an im...
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04/12/2020
Convergence of de Finetti's mixing measure in latent structure models for observed exchangeable sequences
Mixtures of product distributions are a powerful device for learning abo...
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11/15/2019
Learning Models over Relational Data: A Brief Tutorial
This tutorial overviews the state of the art in learning models over rel...
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10/11/2019
Rk-means: Fast Clustering for Relational Data
Conventional machine learning algorithms cannot be applied until a data ...
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09/19/2019
On Efficient Multilevel Clustering via Wasserstein Distances
We propose a novel approach to the problem of multilevel clustering, whi...
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06/20/2019
A Layered Aggregate Engine for Analytics Workloads
This paper introduces LMFAO (Layered Multiple Functional Aggregate Optim...
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05/27/2019
Dirichlet Simplex Nest and Geometric Inference
We propose Dirichlet Simplex Nest, a class of probabilistic models suita...
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01/15/2019
On posterior contraction of parameters and interpretability in Bayesian mixture modeling
We study posterior contraction behaviors for parameters of interest in t...
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12/22/2018
On Functional Aggregate Queries with Additive Inequalities
Motivated by fundamental applications in databases and relational machin...
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11/30/2018
Local inversion-free estimation of spatial Gaussian processes
Maximizing the likelihood has been widely used for estimating the unknow...
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09/24/2018
Streaming dynamic and distributed inference of latent geometric structures
We develop new models and algorithms for learning the temporal dynamics ...
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03/20/2018
AC/DC: In-Database Learning Thunderstruck
We report on the design and implementation of the AC/DC gradient descent...
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10/09/2017
Conic Scan-and-Cover algorithms for nonparametric topic modeling
We propose new algorithms for topic modeling when the number of topics i...
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09/27/2017
Multi-way Interacting Regression via Factorization Machines
We propose a Bayesian regression method that accounts for multi-way inte...
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06/13/2017
Multilevel Clustering via Wasserstein Means
We propose a novel approach to the problem of multilevel clustering, whi...
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10/27/2016
Geometric Dirichlet Means algorithm for topic inference
We propose a geometric algorithm for topic learning and inference that i...
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09/09/2016
Singularity structures and impacts on parameter estimation in finite mixtures of distributions
Singularities of a statistical model are the elements of the model's par...
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01/15/2016
On the consistency of inversion-free parameter estimation for Gaussian random fields
Gaussian random fields are a powerful tool for modeling environmental pr...
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06/03/2015
Optimal change point detection in Gaussian processes
We study the problem of detecting a change in the mean of one-dimensiona...
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01/16/2015
Stochastic Gradient Based Extreme Learning Machines For Online Learning of Advanced Combustion Engines
In this article, a stochastic gradient based online learning algorithm f...
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01/16/2015
Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines
Homogeneous charge compression ignition (HCCI) is a futuristic combustio...
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01/11/2015
Identifiability and optimal rates of convergence for parameters of multiple types in finite mixtures
This paper studies identifiability and convergence behaviors for paramet...
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01/09/2014
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
We present a Bayesian nonparametric framework for multilevel clustering ...
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06/24/2013
Modeling The Stable Operating Envelope For Partially Stable Combustion Engines Using Class Imbalance Learning
Advanced combustion technologies such as homogeneous charge compression ...
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07/06/2012
Sequential detection of multiple change points in networks: a graphical model approach
We propose a probabilistic formulation that enables sequential detection...
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09/15/2011
Convergence of latent mixing measures in finite and infinite mixture models
This paper studies convergence behavior of latent mixing measures that a...
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01/04/2010