56 citations · 61 across the 15 of their papers we have counts for
16 papers
Factor-Assisted Density Estimation: When Ambient Dimension Enables Oracle Rates
Sunrit Chakraborty, David Dunson
We study density estimation in the high-dimensional factor model, whose fixed-dimensional latent factors have an unknown smooth density. We propose factor-assisted density estimati…
Physics-Informed Learning for Robust Acoustic Localization with Calibrated Uncertainty
Jennifer N. Kampe, Changwoo J. Lee, Xin Shen +5
Recent advances in Passive Acoustic Monitoring (PAM) offer an opportunity to obtain ecological spatial point-process data at unprecedented scale. However, realizing this opportunit…
ForestIR: Physics-Informed Forest Sound Simulation for Array-Based Bioacoustic Remote Sensing
Xin Shen, Jennifer N. Kampe, Changwoo J. Lee +7
Microphone array-based passive acoustic monitoring is increasingly used for biodiversity sensing in forests. However, design and evaluation of array systems and configurations rema…
Discovering Spatial Patterns of Readmission Risk Using a Bayesian Competing Risks Model with Spatially Varying Coefficients
Yueming Shen, Christian Pean, David Dunson +1
Time-to-event models are commonly used to study associations between risk factors and disease outcomes in the setting of electronic health records (EHR). In recent years, focus has…
Variational bagging: a robust approach for Bayesian uncertainty quantification
Shitao Fan, Ilsang Ohn, David Dunson +1
Variational Bayes methods are popular due to their computational efficiency and adaptability to diverse applications. In specifying the variational family, mean-field classes are c…
Efficient Bayesian Inference for Discretely Observed Continuous Time Markov Chains
Tao Tang, Lachlan Astfalck, David Dunson
Inference for continuous-time Markov chains (CTMCs) becomes challenging when the process is only observed at discrete time points. The exact likelihood is intractable, and existing…
Adaptive Bayesian Regression on Data with Low Intrinsic Dimensionality
Tao Tang, Nan Wu, Xiuyuan Cheng +1
We study how the posterior contraction rate under a Gaussian process (GP) prior depends on the intrinsic dimension of the predictors and the smoothness of the regression function.…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
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 dat…
Spectral Gap Regularization of Neural Networks
Edric Tam, David Dunson
We introduce Fiedler regularization, a novel approach for regularizing neural networks that utilizes spectral/graphical information. Existing regularization methods often focus on…
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery
Tao Tang, Simon Mak, David Dunson
In many areas of science and engineering, computer simulations are widely used as proxies for physical experiments, which can be infeasible or unethical. Such simulations can often…
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
Yizi Zhang, Meimei Liu, Zhengwu Zhang +1
Mapping of human brain structural connectomes via diffusion MRI offers a unique opportunity to understand brain structural connectivity and relate it to various human traits, such…
Detecting Changes in the Transmission Rate of a Stochastic Epidemic Model
Jenny Huang, Raphaël Morsomme, David Dunson +1
Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease variants, and the introduction of mitigation po…
Interpretable AI for relating brain structural and functional connectomes
Haoming Yang, Steven Winter, Zhengwu Zhang +1
One of the central problems in neuroscience is understanding how brain structure relates to function. Naively one can relate the direct connections of white matter fiber tracts bet…
Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders
Austin Talbot, David Dunson, Kafui Dzirasa +1
Targeted stimulation of the brain has the potential to treat mental illnesses. We propose an approach to help design the stimulation protocol by identifying electrical dynamics acr…
PiPs: a Kernel-based Optimization Scheme for Analyzing Non-Stationary 1D Signals
Jieren Xu, Yitong Li, Haizhao Yang +2
This paper proposes a novel kernel-based optimization scheme to handle tasks in the analysis, e.g., signal spectral estimation and single-channel source separation of 1D non-statio…
Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods
Akihiko Nishimura, David Dunson, Jianfeng Lu
Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation. In this article, we present an extension that can efficiently explore target distributions with di…