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Min-hwan Oh

32 papers at tracked venues · 29 at CORE A* · active 2024–2026

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Venues

NeurIPS ×15ICLR ×6ICML ×5AAAI ×3COLT ×3

Frequent coauthors

Joongkyu Lee
×8
Harin Lee
×5
Jung-hun Kim
×3
Taehyun Hwang
×3
Seok-Jin Kim
×2
Gi-Soo Kim
×2
Seouh-won Yi
×2
Jongyeong Lee
×2
Junya Honda
×2
Shinji Ito
×2
Gyu-Yeol Kim
×1
Hyungkyu Kang
×1

Papers

  1. Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
    COLT 2026 ·
    Harin Lee
    , Min-hwan Oh
  2. ADAM Optimization with Adaptive Batch Selection
    ICLR 2025 ·
    Gyu-Yeol Kim
    , Min-hwan Oh
  3. Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning
    ICLR 2025 ·
    Hyungkyu Kang
    , Min-hwan Oh
  4. Combinatorial Reinforcement Learning with Preference Feedback
    ICML 2025 ·
    Joongkyu Lee
    , Min-hwan Oh
  5. Dynamic Assortment Selection and Pricing with Censored Preference Feedback
    ICLR 2025 ·
    Jung-hun Kim
    , Min-hwan Oh
  6. EUGens: Efficient, Unified and General Dense Layers
    NeurIPS 2025 ·
    Sang Min Kim
    ,
    Byeongchan Kim
    ,
    Arijit Sehanobish
    ,
    Somnath Basu Roy Chowdhury
    ,
    Rahul Kidambi
    ,
    Dongseok Shim
    ,
    Kumar Avinava Dubey
    ,
    Snigdha Chaturvedi
    , Min-hwan Oh,
    Krzysztof Marcin Choromanski
  7. Experimental Design for Semiparametric Bandits
    COLT 2025 ·
    Seok-Jin Kim
    ,
    Gi-Soo Kim
    , Min-hwan Oh
  8. Exploration via Feature Perturbation in Contextual Bandits
    NeurIPS 2025 ·
    Seouh-won Yi
    , Min-hwan Oh
  9. Improved Online Confidence Bounds for Multinomial Logistic Bandits
    ICML 2025 ·
    Joongkyu Lee
    , Min-hwan Oh
  10. Infrequent Exploration in Linear Bandits
    NeurIPS 2025 ·
    Harin Lee
    , Min-hwan Oh
  11. Lasso Bandit with Compatibility Condition on Optimal Arm
    ICLR 2025 ·
    Harin Lee
    ,
    Taehyun Hwang
    , Min-hwan Oh
  12. Linear Bandits with Partially Observable Features
    ICML 2025 ·
    Wonyoung Kim
    ,
    Sungwoo Park
    ,
    Garud Iyengar
    ,
    Assaf Zeevi
    , Min-hwan Oh
  13. Minimax Optimal Reinforcement Learning with Quasi-Optimism
    ICLR 2025 ·
    Harin Lee
    , Min-hwan Oh
  14. Optimal and Practical Batched Linear Bandit Algorithm
    ICML 2025 ·
    Sanghoon Yu
    , Min-hwan Oh
  15. Oracle-Efficient Combinatorial Semi-Bandits
    NeurIPS 2025 ·
    Jung-hun Kim
    ,
    Milan Vojnovic
    , Min-hwan Oh
  16. Position: AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
    NeurIPS 2025 ·
    Eunsu Baek
    ,
    Keondo Park
    ,
    Jeonggil Ko
    , Min-hwan Oh,
    Taesik Gong
    ,
    Hyung-Sin Kim
  17. Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
    NeurIPS 2025 ·
    Joongkyu Lee
    ,
    Seouh-won Yi
    , Min-hwan Oh
  18. Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
    NeurIPS 2025 ·
    Jongyeong Lee
    ,
    Junya Honda
    ,
    Shinji Ito
    , Min-hwan Oh
  19. Symmetry-Aware GFlowNets
    ICML 2025 ·
    Hohyun Kim
    ,
    Seunggeun Lee
    , Min-hwan Oh
  20. Thompson Sampling for Multi-Objective Linear Contextual Bandit
    NeurIPS 2025 ·
    Somangchan Park
    ,
    Heesang Ann
    , Min-hwan Oh
  21. Tractable Multinomial Logit Contextual Bandits with Non-Linear Utilities
    NeurIPS 2025 ·
    Taehyun Hwang
    ,
    Dahngoon Kim
    , Min-hwan Oh
  22. True Impact of Cascade Length in Contextual Cascading Bandits
    NeurIPS 2025 ·
    Hyun-jun Choi
    ,
    Joongkyu Lee
    , Min-hwan Oh
  23. Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
    ICLR 2024 ·
    Joongkyu Lee
    , Min-hwan Oh
  24. Doubly Perturbed Task Free Continual Learning
    AAAI 2024 ·
    Byung Hyun Lee
    , Min-hwan Oh,
    Se Young Chun
  25. Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds
    COLT 2024 ·
    Jongyeong Lee
    ,
    Junya Honda
    ,
    Shinji Ito
    , Min-hwan Oh
  26. Improved Regret of Linear Ensemble Sampling
    NeurIPS 2024 ·
    Harin Lee
    , Min-hwan Oh
  27. Learning Uncertainty-Aware Temporally-Extended Actions
    AAAI 2024 ·
    Joongkyu Lee
    ,
    Seung Joon Park
    ,
    Yunhao Tang
    , Min-hwan Oh
  28. Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
    NeurIPS 2024 ·
    Seok-Jin Kim
    , Min-hwan Oh
  29. Mixed-Effects Contextual Bandits
    AAAI 2024 ·
    Kyungbok Lee
    ,
    Myunghee Cho Paik
    , Min-hwan Oh,
    Gi-Soo Kim
  30. Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
    NeurIPS 2024 ·
    Joongkyu Lee
    , Min-hwan Oh
  31. Queueing Matching Bandits with Preference Feedback
    NeurIPS 2024 ·
    Jung-hun Kim
    , Min-hwan Oh
  32. Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
    NeurIPS 2024 ·
    Wooseong Cho
    ,
    Taehyun Hwang
    ,
    Joongkyu Lee
    , Min-hwan Oh