Publication

Submitted Papers

• Evaluating image generation models via sliced Wasserstein distance
  Hyeok Kyu Kwon, Jaeseung Yang & Minwoo Chae.
  

• Online Bernstein-von Mises theorem [arXiv]
  Jeyong Lee, Junhyeok Choi & Minwoo Chae.
  

• Mitigating spurious correlation via distributionally robust learning with hierarchical ambiguity sets
  Sung Ho Jo, Seonghwi Kim & Minwoo Chae.
  

• A Bayesian approach to contextual dynamic pricing using the proportional hazards model with discrete price data
  Dongguen Kim, Young-Geun Choi & Minwoo Chae.
  

• Nonparametric estimation of a factorizable density using diffusion models [arXiv]
  Hyeok Kyu Kwon, Dongha Kim, Ilsang Ohn & Minwoo Chae.
  

• Online Bayesian inference for Cox proportional hazards model
  Junhyeok Choi, Jeyong Lee, Yongdai Kim & Minwoo Chae.
  

Publications

• A monotone single-index model for spatially-referenced multistate current status data
  Snigdha Das, Minwoo Chae, Debdeep Pati & Dipankar Bandyopadhyay.
  Biometrics. (2025+)

• Advances in Bayesian model selection consistency for high-dimensional generalized linear models [arXiv]
  Jeyong Lee, Minwoo Chae & Ryan Martin.
  Annals of Statistics. (2025+).

• Group-constrained latent Dirichlet allocation for fashion item recommendation
  Seonghwi Kim, Jeyong Lee, Junhyeok Choi, Minwoo Chae, Minseok Song, Jong Hyun Cho & Kyungho Park.
  Journal of the Korean Statistical Society. (2025).

• On reverse inequalities for Besov integral probability metrics between smooth densities
  Jeongjik Lee & Minwoo Chae.
  Statistics & Probability Letters. (2025).

• Rates of convergence for nonparametric estimation of singular distributions using generative adversarial networks [arXiv]
  Jeyong Lee, Hyeok Kyu Kwon & Minwoo Chae.
  Journal of the Korean Statistical Society. (2025).

• Fairness through matching
  Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park & Yongdai Kim.
  Transactions on Machine Learning Research. (2025).

• Using statistical models for optimal packaging in semiconductor manufacturing processes
  Dongguen Kim, Heejin Kim, Yejin Kim, Minwoo Chae, Young Myoung Ko, Young-Mok Bae, Hyungsub Sim, Young Chan Oh & Keum Hwan Noh.
  Journal of the Korean Statistical Society. (2025).

• Wasserstein upper bounds of Lp-norms for multivariate densities in Besov spaces
  Minwoo Chae.
  Statistics & Probability Letters. (2024).

• Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable [arXiv]
  Hyeok Kyu Kwon & Minwoo Chae.
  International Conference on Artificial Intelligence and Statistics (AISTATS). (2024).

• A likelihood approach to nonparametric estimation of a singular distribution using deep generative models [arXiv]
  Minwoo Chae, Dongha Kim, Yongdai Kim & Lizhen Lin.
  Journal of Machine Learning Research. (2023).

• Online learning for Dirichlet process mixture models via weakly conjugate approximation
  Kuhwan Jeong, Minwoo Chae & Yongdai Kim.
  Computational Statistics and Data Analysis. (2023).

• Adaptive Bayesian inference for current status data on a grid
  Minwoo Chae.
  Bernoulli. (2023).

• Posterior asymptotics in Wasserstein metrics on the real line [arXiv]
  Minwoo Chae, Pierpaolo De Blasi & Stephen Walker.
  Electronic Journal of Statistics. (2021).

• Bayesian high-dimensional semi-parametric inference beyond sub-Gaussian errors
  Kyoungjae Lee, Minwoo Chae & Lizhen Lin.
  Journal of the Korean Statistical Society. (2021).

• Wasserstein upper bounds of the total variation for smooth densities
  Minwoo Chae & Stephen Walker.
  Statistics & Probability Letters. (2020).

• An EM-based iterative method for solving large sparse linear systems [arXiv]
  Minwoo Chae & Stephen Walker.
  Linear and Multilinear Algebra. (2020).

• Bayesian sparse linear regression with unknown symmetric error [arXiv]
  Minwoo Chae, Lizhen Lin & David Dunson.
  Information and Inference. (2019).

• The semi-parametric Bernstein-von Mises theorem for regression models with symmetric errors [arXiv]
  Minwoo Chae, Yongdai Kim & Bas Kleijn.
  Statistica Sinica. (2019).

• On an algorithm for solving Fredholm integrals of the first kind [arXiv]
  Minwoo Chae, Ryan Martin & Stephen Walker.
  Statistics and Computing. (2019).

• Additive time-dependent hazard model with doubly truncated data
  Gordon Frank, Minwoo Chae & Yongdai Kim.
  Journal of the Korean Statistical Society. (2019).

• Bayesian consistency for a nonparametric stationary Markov model
  Minwoo Chae & Stephen Walker.
  Bernoulli. (2019).

• Convergence of an iterative algorithm to the nonparametric MLE of a mixing distribution [arXiv]
  Minwoo Chae, Ryan Martin & Stephen Walker.
  Statistics & Probability Letters. (2018).

• A novel approach to Bayesian consistency
  Minwoo Chae & Stephen Walker.
  Electronic Journal of Statistics. (2017).

• An online Gibbs sampler algorithm for hierarchical Dirichlet processes prior
  Yongdai Kim, Minwoo Chae, Kuhwan Jeong, Byungyup Kang & Hyoju Chung.
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD). (2016).

• A closer look at the personality turnover relationship: criterion expansion, dark traits, and time
  Sang Eun Woo, Minwoo Chae, Andrew Jebb & Yongdai Kim.
  Journal of Management. (2016).

• Beta processes and survival analysis
  Yongdai Kim & Minwoo Chae.
  Korean Journal of Applied Statistics. (2014). (Written in Korean)

• Development of high-value traits of dairy cattle using survival analysis
  Kuhwan Jeong, Minwoo Chae, Seulgi Lee, Kwang Hyun Cho & Yongdai Kim.
  Journal of the Korean Data Analysis Society. (2013). (Written in Korean)

• Documents recommendation using large citation data
  Minwoo Chae, Minsoo Kang & Yongdai Kim.
  Journal of the Korean Data and Information Science Society. (2013). (Written in Korean)

• A mixture of beta-Dirichlet processes prior for Bayesian analysis of event history data
  Minwoo Chae, Rafael Weissbach, Kwang Hyun Cho & Yongdai Kim.
  Journal of the Korean Statistical Society. (2013).