Search Results for author:
Found 31 papers, 8 papers with code
Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models
The dendrogram's construction is derived from the theory of convergence of the mixing measures, and as a result, we can both consistently select the true number of mixing components and obtain the pointwise optimal convergence rate for parameter estimation from the tree, even when the model parameters are only weakly identifiable.
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.
Strong identifiability and parameter learning in regression with heterogeneous response
We provide simulation studies and data illustrations, which shed some light on the parameter learning behavior found in several popular regression mixture models reported in the literature.
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
The proposed method learns an individual-specific predictive model from heterogeneous observations, and enables estimation of an optimal transport map that yields a push forward operation onto the demographic features for each task.
Beyond Black Box Densities: Parameter Learning for the Deviated Components
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 complexity of the data set may result in the true density being deviated from the known estimate by a mixture distribution.
Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields
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.
Functional optimal transport: map estimation and domain adaptation for functional data
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.
Robust Unsupervised Learning of Temporal Dynamic Interactions
These distance metrics can serve as an objective for assessing the stability of an interaction learning algorithm.
Rk-means: Fast Clustering for Relational Data
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
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
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.
Functional Aggregate Queries with Additive Inequalities
This new width is sandwiched between the submodular and the fractional hypertree widths.
Scalable inference of topic evolution via models for latent geometric structures
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
In this work we propose a novel approach for learning graph representation of the data using gradients obtained via backpropagation.
Conic Scan-and-Cover algorithms for nonparametric topic modeling
We propose new algorithms for topic modeling when the number of topics is unknown.
Multi-way Interacting Regression via Factorization Machines
We propose a Bayesian regression method that accounts for multi-way interactions of arbitrary orders among the predictor variables.
Multilevel Clustering via Wasserstein Means
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
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.
Singularity structures and impacts on parameter estimation in finite mixtures of distributions
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.
On the consistency of inversion-free parameter estimation for Gaussian random fields
Gaussian random fields are a powerful tool for modeling environmental processes.
Optimal change point detection in Gaussian processes
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.
Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines
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
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
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
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.
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
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
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.
Modeling The Stable Operating Envelope For Partially Stable Combustion Engines Using Class Imbalance Learning
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
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
We study the posterior contraction behavior of the latent population structure that arises in admixture models as the amount of data increases.
Convergence of latent mixing measures in finite and infinite mixture models
This paper studies convergence behavior of latent mixing measures that arise in finite and infinite mixture models, using transportation distances (i. e., Wasserstein metrics).
