David Dunson — accepted AI-conference papers

2024

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson +19

ICML 2024poster

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertai…

Cited by 36 Source ↗PDF ↗Scholar ↗

2021

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

Sean Plummer, Shuang Zhou, Anirban Bhattacharya, David Dunson, Debdeep Pati

AISTATS 2021poster

Transformation based methods have been an attractive approach in non-parametric inference for problems such as unconditioned and conditional density estimation due to their unique hierarchical structure that models the data as flexible transformation of a set of common latent variables. More recentl…

Cited by 4 Source ↗PDF ↗Scholar ↗

2020

Fiedler Regularization: Learning Neural Networks with Graph Sparsity

Edric Tam, David Dunson

ICML 2020poster

We introduce a novel regularization approach for deep learning that incorporates and respects the underlying graphical structure of the neural network. Existing regularization methods often focus on penalizing weights in a global/uniform manner that ignores the connectivity structure of the neural n…

Cited by 20 Source ↗PDF ↗code ↗Scholar ↗

2016

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

Xiangyu Wang, David Dunson, Chenlei Leng

ICML 2016poster

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. For these problems, we advocate the use of a generalized version of OLS motivated by ridge regression, and propose two novel three-step al…

Cited by 27 Source ↗PDF ↗Scholar ↗

2016

Scalable geometric density estimation

Ye Wang, Antonio Canale, David Dunson

AISTATS 2016poster

It is standard to assume a low-dimensional structure in estimating a high-dimensional density. However, popular methods, such as probabilistic principal component analysis, scale poorly computationally. We introduce a novel empirical Bayes method that we term geometric density estimation (GEODE) an…

Cited by 9 Source ↗PDF ↗Scholar ↗

2016

Variational Gaussian Copula Inference

Shaobo Han, Xuejun Liao, David Dunson, Lawrence Carin

AISTATS 2016poster

We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models. For models with continuous and non-Gaussian hidden variables, we propose a semiparametric and automated variational Gaussian copula approach, in which the para…

Cited by 75 Source ↗PDF ↗code ↗Scholar ↗

2015

WASP: Scalable Bayes via barycenters of subset posteriors

Sanvesh Srivastava, Volkan Cevher, Quoc Dinh, David Dunson

AISTATS 2015poster

The promise of Bayesian methods for big data sets has not fully been realized due to the lack of scalable computational algorithms. For massive data, it is necessary to store and process subsets on different machines in a distributed manner. We propose a simple, general, and highly efficient approac…

Cited by 200 Source ↗PDF ↗Scholar ↗