4 citations · 7 across the 11 of their papers we have counts for
11 papers
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…
Transformers Can Learn Posterior Predictive Distributions In-Context
Gyeonghun Kang, Changwoo J. Lee, Xiang Cheng
Prior-data fitted networks (PFNs) have recently emerged as a powerful approach for Bayesian prediction tasks, approximating the posterior predictive distribution (PPD) through in-c…
Scalable and robust regression models for continuous proportional data
Changwoo J. Lee, Benjamin K. Dahl, Otso Ovaskainen +1
Beta regression is used routinely for continuous proportional data, but it often encounters practical issues such as a lack of robustness to misspecification of the beta distributi…
Marginally interpretable spatial logistic regression with bridge processes
Changwoo J. Lee, David B. Dunson
In including random effects to account for dependent observations, the odds ratio interpretation of logistic regression coefficients is changed from population-averaged to subject-…
Logistic-beta processes for dependent random probabilities with beta marginals
Changwoo J. Lee, Alessandro Zito, Huiyan Sang +1
The beta distribution serves as a canonical tool for modeling probabilities in statistics and machine learning. However, there is limited work on flexible and computationally conve…
A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology
Changwoo J. Lee, Elaine Symanski, Amal Rammah +3
Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology for over two decades. Bayesian hierarchical models offer a coheren…
Loss-based Objective and Penalizing Priors for Model Selection Problems
Changwoo J. Lee
Many Bayesian model selection problems, such as variable selection or cluster analysis, start by setting prior model probabilities on a structured model space. Based on a chosen lo…
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems
Hyunwoong Chang, Changwoo J. Lee, Zhao Tang Luo +2
The multiple-try Metropolis (MTM) algorithm is an extension of the Metropolis-Hastings (MH) algorithm by selecting the proposed state among multiple trials according to some weight…
Why the Rich Get Richer? On the Balancedness of Random Partition Models
Changwoo J. Lee, Huiyan Sang
Random partition models are widely used in Bayesian methods for various clustering tasks, such as mixture models, topic models, and community detection problems. While the number o…
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs
Changwoo J. Lee, Zhao Tang Luo, Huiyan Sang
Graphs have been commonly used to represent complex data structures. In models dealing with graph-structured data, multivariate parameters may not only exhibit sparse patterns but…