PUBLICATIONS
Working Papers
D. Kim, S. Shin, K. Song, I. C. Moon, and W. Joo. Adversarial Likelihood-Free Inference on Black-Box Generator. Work in Progress.
D. Kim, K. Song, S. Shin, W. Kang, I. C. Moon, and W. Joo. Sequential Neural Joint Estimation for Likelihood-Free Inference. Under Review.
W. Joo, D. Kim, S. Shin, and I. C. Moon. Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables. Under Review.
(*: Co-corresponding Author, ^: Equal Contribution)
Journal Publications
D. Lee, W. Joo*, and W. C. Kim*. Impact of Changes in Benchmark Constituents on Portfolio Delegation. Industrial Engineering and Management Systems.
J. Lim and W. Joo. Counterfactual Image Generation by Disentangling Data Attributes with Deep Generative Model. Communications for Statistical Applications and Methods, Volume 30 (6), 589-603. 2023.
D. Kim, K. Song, Y. Y. Kim, Y. Shin, W. Kang, I. C. Moon, and W. Joo. Sequential Likelihood-Free Inference with Neural Proposal. Pattern Recognition Letters, Volume 169, 102-109. 2023.
S. Kim, M. Ji, I. C. Moon, and W. Joo. Welfare Program Recommendation by Conditional Variational Autoencoder and Collaborative Filtering. Journal of the Korean Institute of Industrial Engineers, Volume 49 (1), 28-36. 2023.
H. Kong, W. Yun, W. Joo, J. H. Kim, K. K. Kim, I. C. Moon, and W. C. Kim. Constructing Personalized Recommender System for Life Insurance Products with Machine Learning Techniques. Intelligent Systems in Accounting, Finance and Management, Volume 29 (4), 242-253. 2022.
W. Joo, W. Lee, S. Park, and I. C. Moon. Dirichlet Variational Autoencoder. Pattern Recognition, Volume 107, 107514. 2020.
I. Choi^, W. Joo^, and M. Kim^. The Layer Number of α-Evenly Distributed Point Sets. Discrete Mathematics, Volume 343 (10), 112029. 2020.
(*: Co-corresponding Author, ^: Equal Contribution)
Conference Publications
Y. Cho, H. Bae, S. Shin, Y. D. Youn, W. Joo, and I. C. Moon. Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior. AAAI Conference on Artificial Intelligence (AAAI). 2024.
S. Shin, H. S. Bae, D. H. Shin, W. Joo, and I. C. Moon. Loss Curvature Matching for Dataset Selection and Condensation. International Conference on Artificial Intelligence and Statistics Conference (AISTATS). 2023.
H. Kim, S. Shin, J. Jang, K. Song, W. Joo, W. Kang, and I. C. Moon. Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder. AAAI Conference on Artificial Intelligence (AAAI). 2021.
S. Shin, K. Song, J. Jang, H. Kim, W. Joo, and I. C. Moon. Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation. Findings of the Association for Computational Linguistics: Conference on Empirical Methods in Natural Language Processing (Findings of EMNLP). 2020.
B. Na, H. Kim, K. Song, W. Joo, Y. Kim, and I. C. Moon. Deep Generative Positive-Unlabeled Learning under Selection Bias. International Conference on Information and Knowledge Management (CIKM). 2020.
M. Ji, W. Joo, K. Song, Y. Kim, and I. C. Moon. Sequential Recommendation with Relation-Aware Kernelized Self-Attention. AAAI Conference on Artificial Intelligence (AAAI). 2020.
W. Lee, S. Park, W. Joo, and I. C. Moon. Diagnosis Prediction via Medical Context Attention Networks Using Deep Generative Modeling. IEEE International Conference on Data Mining (ICDM). 2018.
(*: Co-corresponding Author, ^: Equal Contribution)
Students' Awards
Jieon Lim. Image Generating using Feature Extraction in Generative Model. The Korean Data & Information Science Society: The Second Best Student Poster Award. 2022 Fall.
Jeongwook Ko. Ensemble-based Uncertainty Quantification with MIMO (Multi-Input Multi-Output) Configuration for Uncertainty-Aware Semi-Supervised Learning. The Korean Data & Information Science Society: The Best Student Poster Award. 2022 Fall.
(*: Co-corresponding Author, ^: Equal Contribution)