Papers
There are 93 references in this bibliography folder.
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.
Youngsoo Jang, Jongmin Lee, and Kee-Eung Kim:
Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues
In: Proceedings of 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.
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.
Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, and Kee-Eung Kim:
Monte-Carlo Tree Search for Constrained POMDPs
In: Proceedings of Neural Information Processing Systems (NeurIPS).
2018.
Kanghoon Lee, Geon-Hyeong Kim, Pedro Ortega, Daniel D. Lee, and Kee-Eung Kim:
Bayesian optimistic Kullback–Leibler exploration
Maching Learning Journal (MLJ), Special Issue on ACML Journal Track.
2018.
Eun Sang Cha, Kee-Eung Kim, Stefano Longo, and Ankur Mehta:
OP-CAS: Collision Avoidance with Overtaking Maneuvers
In: IEEE Intelligent Transport Systems Conference (ITSC).
2018.
MinKu Kang and Kee-Eung Kim:
Simulated Physics for High Speed Aerial Systems
In: International Conference on Control, Automation and Systems (ICCAS).
2018.
Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, and Kee-Eung Kim:
Monte-Carlo Tree Search for Constrained MDPs
In: ICML/IJCAI/AAMAS Workshop on Planning and Learning (PAL).
2018.
Youngsoo Jang, Jiyeon Ham, Byung-Jun Lee, and Kee-Eung Kim:
Cross-language Neural Dialog State Tracker for Large Ontologies using Hierarchical Attention
IEEE/ACM Transactions on Audio, Speech, and Language Processing.
2018.
Jiyeon Ham, Soohyun Lim, Kyeng-Hun Lee, and Kee-Eung Kim:
Extensions to hybrid code networks for FAIR dialog dataset
Computer Speech and Language:12.
2018.
Jang Won Bae, Junseok Lee, Do-Hyung Kim, Kanghoon Lee, Jongmin Lee, Kee-Eung Kim, and Il-Chul Moon:
Layered Behavior Modeling via Combining Descriptive and Prescriptive Approaches: a Case Study of Infantry Company Engagement
IEEE Transactions on System, Man, and Cybernetics: Systems.
2018.
Kee-Eung Kim and Hyun Soo Park:
Imitation Learning via Kernel Mean Embedding
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
2018.
Jiyeon Ham, Soohyun Lim, and Kee-Eung Kim:
Extended Hybrid Code Networks for DSTC6 FAIR Dialog Dataset
In: Dialog System Technology Challenges 6 Workshop.
2017.
Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank Park, and Daniel Lee:
Generative Local Metric Learning for Kernel Regression
In: Proceedings of Neural Information Processing Systems (NIPS).
2017.
Jang Won Bae, Bowon Nam, Kee-Eung Kim, Junseok Lee, and Il-Chul Moon:
Hybrid Modeling and Simulation of Tactical Maneuvers in Computer Generated Force
In: IEEE Conference on System, Man, and Cybernetics (SMC).
2017.
Jongmin Lee, Youngsoo Jang, Pascal Poupart, and Kee-Eung Kim:
Constrained Bayesian Reinforcement Learning via Approximate Linear Programming
In: ECML-PKDD Workshop on Scaling-Up Reinforcement Learning (SURL).
2017.
이종민, 홍정표, 박재영, 이강훈, 김기응, 문일철, and 박재현:
대화력전 및 기계화 보병 시나리오를 통한 대규모 가상군의 POMDP 행동계획 및 학습 사례연구
정보과학회 컴퓨팅의 실제 논문지, 23(6):343-349.
2017.
Jongmin Lee, Youngsoo Jang, Pascal Poupart, and Kee-Eung Kim:
Constrained Bayesian Reinforcement Learning via Approximate Linear Programming
In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI).
2017.
Byung-Jun Lee, Jongmin Lee, and Kee-Eung Kim:
Hierarchically-partitioned Gaussian Process Approximation
In: Proceedings of Artificial Intelligence and Statistics (AISTATS).
2017.
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