Solving Very Large Weakly Coupled Markov Decision Processes. - Nicolas Meuleau; Milos Hauskrecht; Kee-Eung Kim; Leonid Peshkin; Leslie Pack Kaelbling; Thomas L. Dean; Craig Boutilier (AAAI/IAAI, 1998)

Solving Stochastic Planning Problems with Large State and Action Spaces. - Thomas L. Dean; Robert Givan; Kee-Eung Kim (AIPS, 1998)

Solving POMDPs by Searching the Space of Finite Policies. - Nicolas Meuleau; Kee-Eung Kim; Leslie Pack Kaelbling; Anthony R. Cassandra (UAI, 1999)

Learning Finite-State Controllers for Partially Observable Environments. - Nicolas Meuleau; Leonid Peshkin; Kee-Eung Kim; Leslie Pack Kaelbling (UAI, 1999)

Approximate Solutions to Factored Markov Decision Processes via Greedy Search in the Space of Finite State Controllers. - Kee-Eung Kim; Thomas L. Dean; Nicolas Meuleau (AIPS, 2000)

Learning to Cooperate via Policy Search. - Leonid Peshkin; Kee-Eung Kim; Nicolas Meuleau; Leslie Pack Kaelbling (UAI, 2000)

Solving Factored MDPs via Non-Homogeneous Partitioning. - Kee-Eung Kim; Thomas L. Dean (IJCAI, 2001)

Representations and Algorithms for Large Stochastic Planning Problems. - Kee-Eung Kim (2001)

Solving Factored MDPs with Large Action Space Using Algebraic Decision Diagrams. - Kee-Eung Kim; Thomas L. Dean (PRICAI, 2002)

Solving factored MDPs using non-homogeneous partitions. - Kee-Eung Kim; Thomas L. Dean (Artif. Intell., 2003)

Variable bandwidth allocation scheme for energy efficient wireless sensor network. - SeongHwan Cho; Kee-Eung Kim (ICC, 2005)

Hand Grip Pattern Recognition for Mobile User Interfaces. - Kee-Eung Kim; Wook Chang; Sung-Jung Cho; Junghyun Shim; Hyunjeong Lee; Joonah Park; Youngbeom Lee; Sangryoung Kim (AAAI, 2006)

Place Recognition Using Multiple Wearable Cameras. - Kyungmin Min; Seonghun Lee; Kee-Eung Kim; Jin Hyung Kim (UCS, 2007)

Exploiting Symmetries in POMDPs for Point-Based Algorithms. - Kee-Eung Kim (AAAI, 2008)

Symbolic Heuristic Search Value Iteration for Factored POMDPs. - Hyeong Seop Sim; Kee-Eung Kim; Jin Hyung Kim; Du-Seong Chang; Myoung-Wan Koo (AAAI, 2008)

Effects of user modeling on POMDP-based dialogue systems. - Dongho Kim; Hyeong Seop Sim; Kee-Eung Kim; Jin Hyung Kim; Hyunjeong Kim; Joo Won Sung (INTERSPEECH, 2008)

A POMDP approach to P300-based brain-computer interfaces. - Jaeyoung Park; Kee-Eung Kim; Sungho Jo (IUI, 2010)

Point-Based Bounded Policy Iteration for Decentralized POMDPs. - Youngwook Kim; Kee-Eung Kim (PRICAI, 2010)

A POMDP-Based Optimal Control of P300-Based Brain-Computer Interfaces. - Jaeyoung Park; Kee-Eung Kim; Yoon-Kyu Song (AAAI, 2011)

Closing the Gap: Improved Bounds on Optimal POMDP Solutions. - Pascal Poupart; Kee-Eung Kim; Dongho Kim (ICAPS, 2011)

Point-Based Value Iteration for Constrained POMDPs. - Dongho Kim; Jaesong Lee; Kee-Eung Kim; Pascal Poupart (IJCAI, 2011)

MAP Inference for Bayesian Inverse Reinforcement Learning. - Jaedeug Choi; Kee-Eung Kim (NIPS, 2011)

A Geometric Traversal Algorithm for Reward-Uncertain MDPs. - Eunsoo Oh; Kee-Eung Kim (UAI, 2011)

Inverse Reinforcement Learning in Partially Observable Environments. - Jaedeug Choi; Kee-Eung Kim (J. Mach. Learn. Res., 2011)

Robust Performance Evaluation of POMDP-Based Dialogue Systems. - Dongho Kim; Jin H. Kim; Kee-Eung Kim (IEEE ACM Trans. Audio Speech Lang. Process., 2011)

Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions. - Jaedeug Choi; Kee-Eung Kim (NIPS, 2012)

Cost-Sensitive Exploration in Bayesian Reinforcement Learning. - Dongho Kim; Kee-Eung Kim; Pascal Poupart (NIPS, 2012)

Exploiting symmetries for single- and multi-agent Partially Observable Stochastic Domains. - Byung Kon Kang; Kee-Eung Kim (Artif. Intell., 2012)

Bayesian Nonparametric Feature Construction for Inverse Reinforcement Learning. - Jaedeug Choi; Kee-Eung Kim (IJCAI, 2013)

Engineering Statistical Dialog State Trackers: A Case Study on DSTC. - Daejoong Kim; Jaedeug Choi; Kee-Eung Kim; Jungsu Lee; Jinho Sohn (SIGDIAL Conference, 2013)

Optimizing Generative Dialog State Tracker via Cascading Gradient Descent. - Byung-Jun Lee 001; Woosang Lim; Daejoong Kim; Kee-Eung Kim (SIGDIAL Conference, 2014)

Reward Shaping for Model-Based Bayesian Reinforcement Learning. - Hyeoneun Kim; Woosang Lim; Kanghoon Lee; Yung-Kyun Noh; Kee-Eung Kim (AAAI, 2015)

Tighter Value Function Bounds for Bayesian Reinforcement Learning. - Kanghoon Lee; Kee-Eung Kim (AAAI, 2015)

Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes. - Pascal Poupart; Aarti Malhotra; Pei Pei; Kee-Eung Kim; Bongseok Goh; Michael Bowling (AAAI, 2015)

Reactive bandits with attitude. - Pedro A. Ortega; Kee-Eung Kim; Daniel D. Lee (AISTATS, 2015)

Information-Theoretic Bounded Rationality. - Pedro A. Ortega; Daniel A. Braun 001; Justin Dyer; Kee-Eung Kim; Naftali Tishby (CoRR, 2015)

Hierarchical Bayesian Inverse Reinforcement Learning. - Jaedeug Choi; Kee-Eung Kim (IEEE Trans. Cybern., 2015)

Multi-view Automatic Lip-Reading Using Neural Network. - Daehyun Lee; Jongmin Lee 004; Kee-Eung Kim (ACCV Workshops (2), 2016)

Bayesian Reinforcement Learning with Behavioral Feedback. - Teakgyu Hong; Jongmin Lee 004; Kee-Eung Kim; Pedro A. Ortega; Daniel D. Lee (IJCAI, 2016)

Neural dialog state tracker for large ontologies by attention mechanism. - Youngsoo Jang; Jiyeon Ham; Byung-Jun Lee 001; Youngjae Chang 001; Kee-Eung Kim (SLT, 2016)

Dialog History Construction with Long-Short Term Memory for Robust Generative Dialog State Tracking. - Byung-Jun Lee 001; Kee-Eung Kim (Dialogue Discourse, 2016)

Hierarchically-partitioned Gaussian Process Approximation. - Byung-Jun Lee 001; Jongmin Lee 004; Kee-Eung Kim (AISTATS, 2017)

Constrained Bayesian Reinforcement Learning via Approximate Linear Programming. - Jongmin Lee 004; Youngsoo Jang; Pascal Poupart; Kee-Eung Kim (IJCAI, 2017)

Generative Local Metric Learning for Kernel Regression. - Yung-Kyun Noh; Masashi Sugiyama; Kee-Eung Kim; Frank C. Park 001; Daniel D. Lee (NIPS, 2017)

Hybrid modeling and simulation of tactical maneuvers in computer generated force. - Jang Won Bae; Bowon Nam; Kee-Eung Kim; Junseok Lee 001; Il-Chul Moon (SMC, 2017)

Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016). - Robert J. Durrant; Kee-Eung Kim; Geoffrey Holmes 001; Stephen Marsland; Masashi Sugiyama; Zhi-Hua Zhou (Mach. Learn., 2017)

Imitation Learning via Kernel Mean Embedding. - Kee-Eung Kim; Hyun Soo Park (AAAI, 2018)

OP-CAS: Collision Avoidance with Overtaking Maneuvers. - Eun Sang Cha; Kee-Eung Kim; Stefano Longo; Ankur Mehta (ITSC, 2018)

A Bayesian Approach to Generative Adversarial Imitation Learning. - Wonseok Jeon; Seokin Seo; Kee-Eung Kim (NeurIPS, 2018)

Monte-Carlo Tree Search for Constrained POMDPs. - Jongmin Lee 004; Geon-hyeong Kim; Pascal Poupart; Kee-Eung Kim (NeurIPS, 2018)

Cross-Language Neural Dialog State Tracker for Large Ontologies Using Hierarchical Attention. - Youngsoo Jang; Jiyeon Ham; Byung-Jun Lee 001; Kee-Eung Kim (IEEE ACM Trans. Audio Speech Lang. Process., 2018)

Trust Region Sequential Variational Inference. - Geon-hyeong Kim; Youngsoo Jang; Jongmin Lee 004; Wonseok Jeon; Hongseok Yang; Kee-Eung Kim (ACML, 2019)

PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules. - Youngsoo Jang; Jongmin Lee 004; Jaeyoung Park; Kyeng-Hun Lee; Pierre Lison; Kee-Eung Kim (EMNLP/IJCNLP (3), 2019)

Policy Optimization Through Approximated Importance Sampling. - Marcin B. Tomczak; Dongho Kim; Peter Vrancx; Kee-Eung Kim (CoRR, 2019)

Extensions to hybrid code networks for FAIR dialog dataset. - Jiyeon Ham; Soohyun Lim; Kyeng-Hun Lee; Kee-Eung Kim (Comput. Speech Lang., 2019)

A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization. - Yung-Kyun Noh; Ji Young Park; Byoung Geol Choi; Kee-Eung Kim; Seung-Woon Rha (J. Medical Syst., 2019)

Bayesian optimistic Kullback-Leibler exploration. - Kanghoon Lee; Geon-hyeong Kim; Pedro A. Ortega; Daniel D. Lee; Kee-Eung Kim (Mach. Learn., 2019)

Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues. - Youngsoo Jang; Jongmin Lee 004; Kee-Eung Kim (AAAI, 2020)

Residual Neural Processes. - Byung-Jun Lee 001; Seunghoon Hong; Kee-Eung Kim (AAAI, 2020)

Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients. - Jongmin Lee 004; Wonseok Jeon; Geon-Hyeong Kim; Kee-Eung Kim (AAAI, 2020)

End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2. - DongHoon Ham; Jeong-Gwan Lee; Youngsoo Jang; Kee-Eung Kim (ACL, 2020)

Variational Inference for Sequential Data with Future Likelihood Estimates. - Geon-Hyeong Kim; Youngsoo Jang; Hongseok Yang; Kee-Eung Kim (ICML, 2020)

Batch Reinforcement Learning with Hyperparameter Gradients. - Byung-Jun Lee 001; Jongmin Lee 004; Peter Vrancx; Dongho Kim; Kee-Eung Kim (ICML, 2020)

Reinforcement Learning for Control with Multiple Frequencies. - Jongmin Lee 004; Byung-Jun Lee 001; Kee-Eung Kim (NeurIPS, 2020)

Variational Interaction Information Maximization for Cross-domain Disentanglement. - HyeongJoo Hwang; Geon-Hyeong Kim; Seunghoon Hong; Kee-Eung Kim (NeurIPS, 2020)

Foreword: special issue for the journal track of the 12th Asian conference on machine learning (ACML 2020). - Kee-Eung Kim; Vineeth N. Balasubramanian (Mach. Learn., 2020)

Foreword: special issue for the journal track of the 11th Asian Conference on Machine Learning (ACML 2019). - Kee-Eung Kim; Jun Zhu (Mach. Learn., 2020)

Layered Behavior Modeling via Combining Descriptive and Prescriptive Approaches: A Case Study of Infantry Company Engagement. - Jang Won Bae; Junseok Lee 001; Do-Hyung Kim; Kanghoon Lee; Jongmin Lee 004; Kee-Eung Kim; Il-Chul Moon (IEEE Trans. Syst. Man Cybern. Syst., 2020)

Dual Correction Strategy for Ranking Distillation in Top-N Recommender System. - Youngjune Lee; Kee-Eung Kim (CIKM, 2021)

Personalized Treatment Using Biologics: An Analysis Using Counterfactual Regression Based on Deep Learning. - Seongho Eun; Bon San Koo; Ji Seon Oh; Kee-Eung Kim; Byungtae Lee (ICIS, 2021)

Representation Balancing Offline Model-based Reinforcement Learning. - Byung-Jun Lee 001; Jongmin Lee 004; Kee-Eung Kim (ICLR, 2021)

Monte-Carlo Planning and Learning with Language Action Value Estimates. - Youngsoo Jang; Seokin Seo; Jongmin Lee 004; Kee-Eung Kim (ICLR, 2021)

Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic. - Deunsol Yoon; Sunghoon Hong; Byung-Jun Lee 001; Kee-Eung Kim (ICLR, 2021)

OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation. - Jongmin Lee 004; Wonseok Jeon; Byung-Jun Lee 001; Joelle Pineau; Kee-Eung Kim (ICML, 2021)

Multi-View Representation Learning via Total Correlation Objective. - HyeongJoo Hwang; Geon-Hyeong Kim; Seunghoon Hong; Kee-Eung Kim (NeurIPS, 2021)

Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI. - Youngjune Lee; Oh Joon Kwon; Haeju Lee; Joonyoung Kim; Kangwook Lee 004; Kee-Eung Kim (CoRR, 2021)

Corrigendum to 'Extensions to Hybrid Code Networks for FAIR Dialog Data' Computer Speech & Language volume 53 (2019) Pages 80-91. - Jiyeon Ham; Soohyun Lim; Kyeng-Hun Lee; Kee-Eung Kim (Comput. Speech Lang., 2021)

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. - Jongmin Lee 004; Cosmin Paduraru; Daniel J. Mankowitz; Nicolas Heess; Doina Precup; Kee-Eung Kim; Arthur Guez (ICLR, 2022)

Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning. - Sunghoon Hong; Deunsol Yoon; Kee-Eung Kim (ICLR, 2022)

GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems. - Youngsoo Jang; Jongmin Lee 004; Kee-Eung Kim (ICLR, 2022)

DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations. - Geon-Hyeong Kim; Seokin Seo; Jongmin Lee 004; Wonseok Jeon; HyeongJoo Hwang; Hongseok Yang; Kee-Eung Kim (ICLR, 2022)

PAC-Net: A Model Pruning Approach to Inductive Transfer Learning. - Sanghoon Myung; In Huh; Wonik Jang; Jae Myung Choe; Jisu Ryu; Daesin Kim; Kee-Eung Kim; Changwook Jeong (ICML, 2022)

Data Augmentation for Learning to Play in Text-Based Games. - Jinhyeon Kim; Kee-Eung Kim (IJCAI, 2022)

Learning to Embed Multi-Modal Contexts for Situated Conversational Agents. - Haeju Lee; Oh Joon Kwon; Yunseon Choi; Minho Park 002; Ran Han; Yoonhyung Kim; Jinhyeon Kim; Youngjune Lee; Haebin Shin; Kangwook Lee 004; Kee-Eung Kim (NAACL-HLT (Findings), 2022)

LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation. - Geon-Hyeong Kim; Jongmin Lee 004; Youngsoo Jang; Hongseok Yang; Kee-Eung Kim (NeurIPS, 2022)

Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions. - Haanvid Lee; Jongmin Lee 004; Yunseon Choi; Wonseok Jeon; Byung-Jun Lee 001; Yung-Kyun Noh; Kee-Eung Kim (NeurIPS, 2022)

LobsDICE: Offline Imitation Learning from Observation via Stationary Distribution Correction Estimation. - Geon-Hyeong Kim; Jongmin Lee 004; Youngsoo Jang; Hongseok Yang; Kee-Eung Kim (CoRR, 2022)

Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning. - Haeju Lee; Minchan Jeong; Se-Young Yun; Kee-Eung Kim (EMNLP (Findings), 2023)

Information-Theoretic State Space Model for Multi-View Reinforcement Learning. - HyeongJoo Hwang; Seokin Seo; Youngsoo Jang; Sungyoon Kim; Geon-Hyeong Kim; Seunghoon Hong; Kee-Eung Kim (ICML, 2023)

AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation. - Daiki E. Matsunaga; Jongmin Lee 004; Jaeseok Yoon; Stefanos Leonardos; Pieter Abbeel; Kee-Eung Kim (NeurIPS, 2023)

Regularized Behavior Cloning for Blocking the Leakage of Past Action Information. - Seokin Seo; HyeongJoo Hwang; Hongseok Yang; Kee-Eung Kim (NeurIPS, 2023)

Adapting Text-based Dialogue State Tracker for Spoken Dialogues. - Jaeseok Yoon; Seunghyun Hwang; Ran Han; Jeonguk Bang; Kee-Eung Kim (CoRR, 2023)

Trustworthy residual vehicle value prediction for auto finance. - Mihye Kim; Jimyung Choi; Jaehyun Kim; Wooyoung Kim; Yeonung Baek; Gisuk Bang; Kwangwoon Son; Yeonman Ryou; Kee-Eung Kim (AI Mag., 2023)

Stitching Sub-trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL. - Sungyoon Kim; Yunseon Choi; Daiki E. Matsunaga; Kee-Eung Kim (AAAI, 2024)

A Submodular Optimization Approach to Accountable Loan Approval. - Kyungsik Lee; Hana Yoo; Sumin Shin; Wooyoung Kim; Yeonung Baek; Hyunjin Kang; Jaehyun Kim; Kee-Eung Kim (AAAI, 2024)

Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL. - Yunseon Choi; Sangmin Bae; Seonghyun Ban; Minchan Jeong; Chuheng Zhang; Lei Song 001; Li Zhao 007; Jiang Bian 002; Kee-Eung Kim (ACL (1), 2024)

GDPO: Learning to Directly Align Language Models with Diversity Using GFlowNets. - Oh Joon Kwon; Daiki E. Matsunaga; Kee-Eung Kim (EMNLP, 2024)

Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies. - Haanvid Lee; Tri Wahyu Guntara; Jongmin Lee 004; Yung-Kyun Noh; Kee-Eung Kim (ICLR, 2024)

Diversification of Adaptive Policy for Effective Offline Reinforcement Learning. - Yunseon Choi; Li Zhao 007; Chuheng Zhang; Lei Song 001; Jiang Bian 002; Kee-Eung Kim (IJCAI, 2024)

SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token Synchronization. - Youngjin Ahn; Jungwoo Park; Sangha Park; Jonghyun Choi; Kee-Eung Kim (INTERSPEECH, 2024)

Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning. - Seonghyun Ban; Heesan Kong; Kee-Eung Kim (NeurIPS, 2024)

Mitigating Covariate Shift in Behavioral Cloning via Robust Stationary Distribution Correction. - Seokin Seo; Byung-Jun Lee 001; Jongmin Lee 004; HyeongJoo Hwang; Hongseok Yang; Kee-Eung Kim (NeurIPS, 2024)

A submodular optimization approach to trustworthy loan approval automation. - Kyungsik Lee; Hana Yoo; Sumin Shin; Wooyoung Kim; Yeonung Baek; Hyunjin Kang; Jaehyun Kim; Kee-Eung Kim (AI Mag., 2024)

Zero-Shot Multi-Hop Question Answering via Monte-Carlo Tree Search with Large Language Models. - Seongmin Lee 011; Jaewook Shin; Youngjin Ahn; Seokin Seo; Ohjoon Kwon; Kee-Eung Kim (CoRR, 2024)

Monet: Mixture of Monosemantic Experts for Transformers. - Jungwoo Park; Youngjin Ahn; Kee-Eung Kim; Jaewoo Kang (CoRR, 2024)

Monet: Mixture of Monosemantic Experts for Transformers. - Jungwoo Park; Ahn Young Jin; Kee-Eung Kim; Jaewoo Kang (ICLR, 2025)

Goal-Conditioned DPO: Prioritizing Safety in Misaligned Instructions. - Joo Bon Maeng; Seongmin Lee 011; Seokin Seo; Kee-Eung Kim (NAACL (Long Papers), 2025)