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Found 55 papers, 15 papers with code
Logistic-beta processes for modeling dependent random probabilities with beta marginals
The beta distribution serves as a canonical tool for modeling probabilities and is extensively used in statistics and machine learning, especially in the field of Bayesian nonparametrics.
Bayesian Transfer Learning
Transfer learning is a burgeoning concept in statistical machine learning that seeks to improve inference and/or predictive accuracy on a domain of interest by leveraging data from related domains.
Proximal Algorithms for Accelerated Langevin Dynamics
We develop a novel class of MCMC algorithms based on a stochastized Nesterov scheme.
Sequential Gibbs Posteriors with Applications to Principal Component Analysis
Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter weighting information in the loss relative to the prior and providing a control of posterior uncertainty.
Machine Learning and the Future of Bayesian Computation
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information.
Ellipsoid fitting with the Cayley transform
We introduce Cayley transform ellipsoid fitting (CTEF), an algorithm that uses the Cayley transform to fit ellipsoids to noisy data in any dimension.
Covariate-informed latent interaction models: Addressing geographic & taxonomic bias in predicting bird-plant interactions
Reductions in natural habitats urge that we better understand species' interconnection and how biological communities respond to environmental changes.
Methodology
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds
One of the key challenges for optimization on manifolds is the difficulty of verifying the complexity of the objective function, e. g., whether the objective function is convex or non-convex, and the degree of non-convexity.
Extended Stochastic Block Models with Application to Criminal Networks
The coexistence of these noisy block patterns limits the reliability of routinely-used community detection algorithms, and requires extensions of model-based solutions to realistically characterize the node partition process, incorporate information from node attributes, and provide improved strategies for estimation and uncertainty quantification.
A generalized Bayes framework for probabilistic clustering
Several existing clustering algorithms, including k-means, can be interpreted as generalized Bayes estimators under our framework, and hence we provide a method of uncertainty quantification for these approaches.
Classification Trees for Imbalanced and Sparse Data: Surface-to-Volume Regularization
When data are limited in one or more of the classes, the estimated decision boundaries are often irregularly shaped due to the limited sample size, leading to poor generalization error.
Composite mixture of log-linear models for categorical data
Multivariate categorical data are routinely collected in many application areas.
Methodology
Nearest Neighbor Dirichlet Mixtures
There is a rich literature on Bayesian methods for density estimation, which characterize the unknown density as a mixture of kernels.
Domain Adaptive Bootstrap Aggregating
When there is a distributional shift between data used to train a predictive algorithm and current data, performance can suffer.
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets
In this paper, building on recent advances in deep learning, we develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer the relationships between brain structural connectomes and human traits.
Identifying main effects and interactions among exposures using Gaussian processes
Our interest is in model selection for these different components, accounting for uncertainty and addressing non-identifability between the linear and nonparametric components of the semiparametric model.
Applications
Perturbed factor analysis: Improving generalizability across studies
As an alternative, we propose a class of Perturbed Factor Analysis (PFA) models that assume a common factor structure across studies after perturbing the data via multiplication by a study-specific matrix.
Methodology
Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation
Posterior computation for high-dimensional data with many parameters can be challenging.
Computation Methodology
Geodesic Distance Estimation with Spherelets
When the manifold is unknown, it is challenging to accurately approximate the geodesic distance.
Bayesian Factor Analysis for Inference on Interactions
This article is motivated by the problem of inference on interactions among chemical exposures impacting human health outcomes.
Methodology Applications
Lipschitz Bandit Optimization with Improved Efficiency
We consider the Lipschitz bandit optimization problem with an emphasis on practical efficiency.
Classification via local manifold approximation
It is challenging to obtain accurate classification performance when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports.
Bayesian cumulative shrinkage for infinite factorizations
There is a wide variety of models in which the dimension of the parameter space is unknown.
Methodology
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks
We introduce a multiscale supervised dimension reduction method for SPatial Interaction Network (SPIN) data, which consist of a collection of spatially coordinated interactions.
Bayesian Distance Clustering
Model-based clustering is widely-used in a variety of application areas.
Bayesian Modular and Multiscale Regression
We tackle the problem of multiscale regression for predictors that are spatially or temporally indexed, or with a pre-specified multiscale structure, with a Bayesian modular approach.
Methodology
Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks
This article is motivated by soccer positional passing networks collected across multiple games.
Bayesian Multi Plate High Throughput Screening of Compounds
High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candidate hits.
Efficient Manifold and Subspace Approximations with Spherelets
There is a rich literature on approximating the unknown manifold, and on exploiting such approximations in clustering, data compression, and prediction.
Boosting Variational Inference
Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closest distribution to the exact posterior over some family of distributions.
Fast moment estimation for generalized latent Dirichlet models
We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application of MELD to several data sets.
Variational Gaussian Copula Inference
We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models.
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning.
Parallelizing MCMC with Random Partition Trees
The new algorithm applies random partition trees to combine the subset posterior draws, which is distribution-free, easy to resample from and can adapt to multiple scales.
Locally Adaptive Dynamic Networks
Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals.
On the consistency theory of high dimensional variable screening
Variable screening is a fast dimension reduction technique for assisting high dimensional feature selection.
Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics
In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time from the planned arrival time.
Compressed Gaussian Process
Nonparametric regression for massive numbers of samples (n) and features (p) is an increasingly important problem.
Robust and Scalable Bayes via a Median of Subset Posterior Measures
We propose a novel approach to Bayesian analysis that is provably robust to outliers in the data and often has computational advantages over standard methods.
Minimax Optimal Bayesian Aggregation
It is generally believed that ensemble approaches, which combine multiple algorithms or models, can outperform any single algorithm at machine learning tasks, such as prediction.
Bayesian Conditional Density Filtering
We propose a Conditional Density Filtering (C-DF) algorithm for efficient online Bayesian inference.
Parallelizing MCMC via Weierstrass Sampler
With the rapidly growing scales of statistical problems, subset based communication-free parallel MCMC methods are a promising future for large scale Bayesian analysis.
Multiscale Dictionary Learning for Estimating Conditional Distributions
Nonparametric estimation of the conditional distribution of a response given high-dimensional features is a challenging problem.
Locally Adaptive Bayesian Multivariate Time Series
In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process.
Nonparametric Bayes dynamic modeling of relational data
Symmetric binary matrices representing relations among entities are commonly collected in many areas.
Learning Densities Conditional on Many Interacting Features
Learning a distribution conditional on a set of discrete-valued features is a commonly encountered task.
Bayesian Compressed Regression
As an alternative to variable selection or shrinkage in high dimensional regression, we propose to randomly compress the predictors prior to analysis.
Multiresolution Gaussian Processes
We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes.
Repulsive Mixtures
One important issue that arises in using discrete mixtures is low separation in the components; in particular, different components can be introduced that are very similar and hence redundant.
Locally adaptive factor processes for multivariate time series
In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process.
Generalized Beta Mixtures of Gaussians
In recent years, a rich variety of shrinkage priors have been proposed that have great promise in addressing massive regression problems.
Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices
The nested Chinese restaurant process is extended to design a nonparametric topic-model tree for representation of human choices.
The Kernel Beta Process
A new Le ́vy process prior is proposed for an uncountable collection of covariate- dependent feature-learning measures; the model is called the kernel beta process (KBP).
Joint Analysis of Time-Evolving Binary Matrices and Associated Documents
An objective of such analysis is to infer structure and inter-relationships underlying the matrices, here defined by latent features associated with each axis of the matrix.
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation
The model clusters the images into classes, and each image is segmented into a set of objects, also allowing the opportunity to assign a word to each object (localized labeling).
