Papers

Jinhyeon Kim, Donghoon Ham, Jeong-Gwan Lee, and Kee-Eung Kim: End-to-End Document-Grounded Conversation with Encoder-Decoder Pre-Trained Language Model
In: AAAI Conference on Artificial Intelligence (AAAI) DSTC9 Workshop. 2021.

Deunsol Yoon*, Sunghoon Hong*, Byung-Jun Lee, and Kee-Eung Kim: Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic
In: International Conference on Learning Representations (ICLR). 2021.

Youngsoo Jang, Seokin Seo, Jongmin Lee, and Kee-Eung Kim: Monte-Carlo Planning and Learning with Language Action Value Estimates
In: International Conference on Learning Representations (ICLR). 2021.

Byung-Jun Lee, Jongmin Lee, and Kee-Eung Kim: Representation Balancing Offline Model-based Reinforcement Learning
In: International Conference on Learning Representations (ICLR). 2021.

이병준, 이종민, 최윤선, 장영수, and 김기응: 효율적인 평생학습 알고리즘의 모델기반 강화학습 적용에 관한 연구
In: 한국소프트웨어종합학술대회, 한국정보과학회. 2020.

HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, and Kee-Eung Kim: Variational Interaction Information Maximization for Cross-domain Disentanglement
In: Proceedings of Neural Information Processing Systems (NeurIPS). 2020.

Jongmin Lee, Byung-Jun Lee, and Kee-Eung Kim: Reinforcement Learning for Control with Multiple Frequencies
In: Proceedings of Neural Information Processing Systems (NeurIPS). 2020.

Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, and Kee-Eung Kim: Variational Inference for Sequential Data with Future Likelihood Estimates
In: Proceedings of the International Conference on Machine Learning (ICML). 2020.

Byung-Jun Lee*, Jongmin Lee*, Peter Vrancx, Dongho Kim, and Kee-Eung Kim: Batch Reinforcement Learning with Hyperparameter Gradients
In: Proceedings of the International Conference on Machine Learning (ICML). 2020.

Donghoon Ham*, Jeong-Gwan Lee*, Youngsoo Jang, and Kee-Eung Kim: End-to-End Neural Pipeline for Goal-Oriented Dialogue System using GPT-2
In: Annual Conference of the Association for Computational Linguistics (ACL). 2020.

Donghoon Ham*, Jeong-Gwan Lee*, Youngsoo Jang, and Kee-Eung Kim: End-to-End Neural Pipeline for Goal-Oriented Dialogue System using GPT-2
In: AAAI Conference on Artificial Intelligence (AAAI) DSTC8 Workshop. 2020.

Byung-Jun Lee, Seunghoon Hong, and Kee-Eung Kim: Residual Neural Processes
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 2020.

Youngsoo Jang, Jongmin Lee, and Kee-Eung Kim: Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 2020.

Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, and Kee-Eung Kim: Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 2020.

김건형, 장영수, 이종민, and 김기응: 몬테 카를로 목표를 위한 분산 감소 방법
한국통신학회 하계종합학술발표회. 2019.

이종민, 김건형, and 김기응: 연속 행동공간에서의 몬테-카를로 트리 탐색에 관한 연구
한국통신학회 하계종합학술발표회. 2019.

Nianyin Zeng, Zidong Wang, Hong Zhang, Kee-Eung Kim, Yurong Li, and Xiaohui Liu: An Improved Particle Filter With a Novel Hybrid Proposal Distribution for Quantitative Analysis of Gold Immunochromatographic Strips
IEEE Transactions on Nanotechnology, 18:819-829. 2019.

Yung-Kyun Noh, Ji Young Park, Byoung Geol Choi, Kee-Eung Kim, and Seung-Woon Rha: A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization
Journal of Medical Systems. 2019.

Youngsoo Jang, Jongmin Lee, and Kee-Eung Kim: Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues
In: Neural Information Processing Systems (NeurIPS) Conversational AI workshop. 2019.

Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, Wonseok Jeon, Hongseok Yang, and Kee-Eung Kim: Trust Region Sequential Variational Inference
In: Proceedings of Asian Conference on Machine Learning (ACML). 2019.

Youngsoo Jang*, Jongmin Lee*, Jaeyoung Park*, Kyeng-Hun Lee, Pierre Lison, and Kee-Eung Kim: PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP) System Demonstrations. 2019.

강민구 and 김기응: 강화학습을 이용한 초고속비행체 제어기 학습
In: 한국군사과학기술학회 종합학술대회. 2019.

Kanghoon Lee, Geon-Hyeong Kim, Pedro Ortega, Daniel D. Lee, and Kee-Eung Kim: Bayesian optimistic Kullback–Leibler exploration
Machine Learning Journal (MLJ), 108. 2019.

김건형, 장영수, 이종민, and 김기응: 모델 기반 베이지안 강화학습의 연속된 도메인으로의 확장에 대한 연구
한국통신학회 하계종합학술발표회 논문집. 2018.

Wonseok Jeon, Seokin Seo, and Kee-Eung Kim: A Bayesian Approach to Generative Adversarial Imitation Learning
In: Proceedings of Neural Information Processing Systems (NeurIPS). 2018.