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. Neural Posterior Regularization for Likelihood-Free Inference. Under Review.

• D. Lee, W. Joo*, and W. C. Kim*. Impact of Changes in Benchmark Constituents on Delegated Fund Managers. Under Review. (*: Co-corresponding Author)

• W. Joo, D. Kim, S. Shin, and I. C. Moon. Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables. Under Review.

Journal Publications

• 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. (^: 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. 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: 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. International Conference on Data Mining (ICDM). 2018.