| 2026 | COLT | Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning. | Harin Lee, Min-hwan Oh |
| 2025 | COLT | Experimental Design for Semiparametric Bandits. | Seok-Jin Kim, Gi-Soo Kim, Min-hwan Oh |
| 2025 | ICLR | Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning. | Hyungkyu Kang, Min-hwan Oh |
| 2025 | ICLR | ADAM Optimization with Adaptive Batch Selection. | Gyu-Yeol Kim, Min-hwan Oh |
| 2025 | ICLR | Dynamic Assortment Selection and Pricing with Censored Preference Feedback. | Jung-hun Kim, Min-hwan Oh |
| 2025 | ICLR | Lasso Bandit with Compatibility Condition on Optimal Arm. | Harin Lee, Taehyun Hwang, Min-hwan Oh |
| 2025 | ICLR | Minimax Optimal Reinforcement Learning with Quasi-Optimism. | Harin Lee, Min-hwan Oh |
| 2025 | ICML | Symmetry-Aware GFlowNets. | Hohyun Kim, Seunggeun Lee, Min-hwan Oh |
| 2025 | ICML | Linear Bandits with Partially Observable Features. | Wonyoung Kim, Sungwoo Park, Garud Iyengar, Assaf Zeevi, Min-hwan Oh |
| 2025 | ICML | Improved Online Confidence Bounds for Multinomial Logistic Bandits. | Joongkyu Lee, Min-hwan Oh |
| 2025 | ICML | Combinatorial Reinforcement Learning with Preference Feedback. | Joongkyu Lee, Min-hwan Oh |
| 2025 | ICML | Optimal and Practical Batched Linear Bandit Algorithm. | Sanghoon Yu, Min-hwan Oh |
| 2024 | AAAI | Doubly Perturbed Task Free Continual Learning. | Byung Hyun Lee, Min-hwan Oh, Se Young Chun |
| 2024 | AAAI | Mixed-Effects Contextual Bandits. | Kyungbok Lee, Myunghee Cho Paik, Min-hwan Oh, Gi-Soo Kim |
| 2024 | AAAI | Learning Uncertainty-Aware Temporally-Extended Actions. | Joongkyu Lee, Seung Joon Park, Yunhao Tang, Min-hwan Oh |
| 2024 | COLT | Follow-the-Perturbed-Leader with Frchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds. | Jongyeong Lee, Junya Honda, Shinji Ito, Min-hwan Oh |
| 2024 | ICLR | Demystifying Linear MDPs and Novel Dynamics Aggregation Framework. | Joongkyu Lee, Min-hwan Oh |
| 2023 | AAAI | Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation. | Taehyun Hwang, Min-hwan Oh |
| 2023 | AISTATS | Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits. | Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh |
| 2023 | ICML | Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model. | Young-Geun Choi, Gi-Soo Kim, Yunseo Choi, Wooseong Cho, Myunghee Cho Paik, Min-hwan Oh |
| 2023 | ICML | Combinatorial Neural Bandits. | Taehyun Hwang, Kyuwook Chai, Min-hwan Oh |
| 2023 | ICML | Model-based Offline Reinforcement Learning with Count-based Conservatism. | Byeongchan Kim, Min-hwan Oh |
| 2022 | WWW | Stochastic-Expert Variational Autoencoder for Collaborative Filtering. | Yoon-Sik Cho, Min-hwan Oh |
| 2021 | AAAI | Multinomial Logit Contextual Bandits: Provable Optimality and Practicality. | Min-hwan Oh, Garud Iyengar |
| 2021 | ICML | Sparsity-Agnostic Lasso Bandit. | Min-hwan Oh, Garud Iyengar, Assaf Zeevi |
| 2020 | AAAI | Crowd Counting with Decomposed Uncertainty. | Min-hwan Oh, Peder A. Olsen, Karthikeyan Natesan Ramamurthy |
| 2019 | KDD | Sequential Anomaly Detection using Inverse Reinforcement Learning. | Min-hwan Oh, Garud Iyengar |
| 2018 | HiPC | Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs. | Hiroki Kanezashi, Toyotaro Suzumura, Dario Garcia-Gasulla, Min-hwan Oh, Satoshi Matsuoka |