665 citations · 942 across the 38 of their papers we have counts for
44 papers
Stable Causal Estimation with Optimal Transport
Yidan Xu, Yixin Wang, XuanLong Nguyen
In observational studies, the overlap assumption, also called positivity, is frequently strained in practice. Limited overlap destabilizes widely used estimators such as inverse pr…
Minimum -distance estimators for finite mixing measures
Yun Wei, Sayan Mukherjee, XuanLong Nguyen
Finite mixture models have long been used across a variety of fields in engineering and sciences. Recently there has been a great deal of interest in quantifying the convergence be…
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
Jiacheng Zhu, Jielin Qiu, Aritra Guha +4
We propose to study and promote the robustness of a model as per its performance through the interpolation of training data distributions. Specifically, (1) we augment the data by…
Strong identifiability and parameter learning in regression with heterogeneous response
Dat Do, Linh Do, XuanLong Nguyen
Mixtures of regression are a powerful class of models for regression learning with respect to a highly uncertain and heterogeneous response variable of interest. In addition to bei…
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
Jiacheng Zhu, Gregory Darnell, Agni Kumar +4
Heart rate variability (HRV) is a practical and noninvasive measure of autonomic nervous system activity, which plays an essential role in cardiovascular health. However, using HRV…
Beyond Black Box Densities: Parameter Learning for the Deviated Components
Dat Do, Nhat Ho, XuanLong Nguyen
As we collect additional samples from a data population for which a known density function estimate may have been previously obtained by a black box method, the increased complexit…
Modeling Random Directions in 2D Simplex Data
Rayleigh Lei, XuanLong Nguyen
We propose models and algorithms for learning about random directions in two-dimensional simplex data, and apply our methods to the study of income level proportions and their chan…
Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields
Sunrit Chakraborty, Aritra Guha, Rayleigh Lei +1
Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to…
Functional optimal transport: map estimation and domain adaptation for functional data
Jiacheng Zhu, Aritra Guha, Dat Do +3
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 te…
Robust Unsupervised Learning of Temporal Dynamic Interactions
Aritra Guha, Rayleigh Lei, Jiacheng Zhu +2
Robust representation learning of temporal dynamic interactions is an important problem in robotic learning in general and automated unsupervised learning in particular. Temporal d…
Convergence of de Finetti's mixing measure in latent structure models for observed exchangeable sequences
Yun Wei, XuanLong Nguyen
Mixtures of product distributions are a powerful device for learning about heterogeneity within data populations. In this class of latent structure models, de Finetti's mixing meas…
Learning Models over Relational Data: A Brief Tutorial
Maximilian Schleich, Dan Olteanu, Mahmoud Abo-Khamis +2
This tutorial overviews the state of the art in learning models over relational databases and makes the case for a first-principles approach that exploits recent developments in da…
Rk-means: Fast Clustering for Relational Data
Ryan Curtin, Ben Moseley, Hung Q. Ngo +3
Conventional machine learning algorithms cannot be applied until a data matrix is available to process. When the data matrix needs to be obtained from a relational database via a f…
On Efficient Multilevel Clustering via Wasserstein Distances
Viet Huynh, Nhat Ho, Nhan Dam +4
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 pote…
A Layered Aggregate Engine for Analytics Workloads
Maximilian Schleich, Dan Olteanu, Mahmoud Abo Khamis +2
This paper introduces LMFAO (Layered Multiple Functional Aggregate Optimization), an in-memory optimization and execution engine for batches of aggregates over the input database.…
Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin, Aritra Guha, Yuekai Sun +1
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 f…
On posterior contraction of parameters and interpretability in Bayesian mixture modeling
Aritra Guha, Nhat Ho, XuanLong Nguyen
We study posterior contraction behaviors for parameters of interest in the context of Bayesian mixture modeling, where the number of mixing components is unknown while the model it…
Functional Aggregate Queries with Additive Inequalities
Mahmoud Abo Khamis, Ryan R. Curtin, Benjamin Moseley +4
Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some…
Local inversion-free estimation of spatial Gaussian processes
Hossein Keshavarz, XuanLong Nguyen, Clayton Scott
Maximizing the likelihood has been widely used for estimating the unknown covariance parameters of spatial Gaussian processes. However, evaluating and optimizing the likelihood fun…
Scalable inference of topic evolution via models for latent geometric structures
Mikhail Yurochkin, Zhiwei Fan, Aritra Guha +2
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. Our model is…
AC/DC: In-Database Learning Thunderstruck
Mahmoud Abo Khamis, Hung Q. Ngo, XuanLong Nguyen +2
We report on the design and implementation of the AC/DC gradient descent solver for a class of optimization problems over normalized databases. AC/DC decomposes an optimization pro…
Conic Scan-and-Cover algorithms for nonparametric topic modeling
Mikhail Yurochkin, Aritra Guha, XuanLong Nguyen
We propose new algorithms for topic modeling when the number of topics is unknown. Our approach relies on an analysis of the concentration of mass and angular geometry of the topic…
Multi-way Interacting Regression via Factorization Machines
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. Our model makes use of a factorization mechanism…
Robust estimation of mixing measures in finite mixture models
Nhat Ho, XuanLong Nguyen, Ya'acov Ritov
In finite mixture models, apart from underlying mixing measure, true kernel density function of each subpopulation in the data is, in many scenarios, unknown. Perhaps the most popu…