Search Results for author: David Dunson

Found 18 papers, 5 papers with code

Gaussian Process Subspace Regression for Model Reduction

1 code implementation9 Jul 2021 Ruda Zhang, Simon Mak, David Dunson

In PROM, each parameter point can be associated with a subspace, which is used for Petrov-Galerkin projections of large system matrices.

Model Selection

Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference

no code implementations23 Oct 2020 Sean Plummer, Shuang Zhou, Anirban Bhattacharya, David Dunson, Debdeep Pati

More recently, transformation-based models have been used in variational inference (VI) to construct flexible implicit families of variational distributions.

Density Estimation Hierarchical structure +2

Bayesian neural networks and dimensionality reduction

no code implementations18 Aug 2020 Deborshee Sen, Theodore Papamarkou, David Dunson

We attempt to solve these problems by deploying Markov chain Monte Carlo sampling algorithms (MCMC) for Bayesian inference in ANN models with latent variables.

Bayesian Inference Dimensionality Reduction +1

Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering

no code implementations17 Aug 2020 Debolina Paul, Saptarshi Chakraborty, Didong Li, David Dunson

In a rich variety of real data clustering applications, PEA is shown to do as well as k-means for simple datasets, while dramatically improving performance in more complex settings.

Dimensionality Reduction

Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity

1 code implementation10 Apr 2020 Austin Talbot, David Dunson, Kafui Dzirasa, David Carlson

Joint factor models for the predictors and outcomes are natural, but maximum likelihood estimates of these models can struggle in practice when there is model misspecification.

Dimensionality Reduction

Fiedler Regularization: Learning Neural Networks with Graph Sparsity

no code implementations ICML 2020 Edric Tam, David Dunson

We propose to use the Fiedler value of the neural network's underlying graph as a tool for regularization.

Bayesian joint modeling of chemical structure and dose response curves

1 code implementation27 Dec 2019 Kelly R. Moran, David Dunson, Amy H. Herring

Our model also enables the prediction of chemical dose-response profiles based on chemical structure (that is, without in vivo or in vitro testing) by taking advantage of a large database of chemicals that have already been tested for toxicity in HTS programs.


Bayesian Hierarchical Factor Regression Models to Infer Cause of Death From Verbal Autopsy Data

1 code implementation20 Aug 2019 Kelly R. Moran, Elizabeth L. Turner, David Dunson, Amy H. Herring

In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations.


Stochastic Lipschitz Q-Learning

no code implementations24 Apr 2019 Xu Zhu, David Dunson

To the best of our knowledge, this is the first analysis in the model-free setting whose established regret matches the lower bound up to a logarithmic factor.


Non-Oscillatory Pattern Learning for Non-Stationary Signals

no code implementations21 May 2018 Jieren Xu, Yitong Li, David Dunson, Ingrid Daubechies, Haizhao Yang

This paper proposes a novel non-oscillatory pattern (NOP) learning scheme for several oscillatory data analysis problems including signal decomposition, super-resolution, and signal sub-sampling.


Reducing over-clustering via the powered Chinese restaurant process

no code implementations15 Feb 2018 Jun Lu, Meng Li, David Dunson

Dirichlet process mixture (DPM) models tend to produce many small clusters regardless of whether they are needed to accurately characterize the data - this is particularly true for large data sets.

Intrinsic Gaussian processes on complex constrained domains

no code implementations3 Jan 2018 Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil Lawrence, David Dunson

in-GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces.

Gaussian Processes

Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods

1 code implementation23 May 2017 Akihiko Nishimura, David Dunson, Jianfeng Lu

Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation.


Adaptive posterior convergence rates in non-linear latent variable models

no code implementations26 Jan 2017 Shuang Zhou, Debdeep Pati, Anirban Bhattacharya, David Dunson

In this article, we study rates of posterior contraction in univariate density estimation for a class of non-linear latent variable models where unobserved U(0, 1) latent variables are related to the response variables via a random non-linear regression with an additive error.

Statistics Theory Statistics Theory

DECOrrelated feature space partitioning for distributed sparse regression

no code implementations NeurIPS 2016 Xiangyu Wang, David Dunson, Chenlei Leng

The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space).

Variable Selection

No penalty no tears: Least squares in high-dimensional linear models

no code implementations7 Jun 2015 Xiangyu Wang, David Dunson, Chenlei Leng

Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size.

Median Selection Subset Aggregation for Parallel Inference

no code implementations NeurIPS 2014 Xiangyu Wang, Peichao Peng, David Dunson

For massive data sets, efficient computation commonly relies on distributed algorithms that store and process subsets of the data on different machines, minimizing communication costs.

Feature Selection Model Selection +1

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