Skip to content
conftrace_

Min-hwan Oh

32 papers · 2019–2026 · 6 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

Jump to papers ↓
+5 more ↓ ๐Ÿƒ Academic Marathon ยท Sprint ๐ŸŒ Conference Polyglot ยท Silver ๐Ÿ—บ๏ธ Taxonomy Completionist (45) ๐ŸŒ‰ Interdisciplinary Bridge ยท Silver ๐Ÿ† Keyword Champion (2)
๐Ÿ† Grand Slam ๐Ÿ”ฌ Deep Specialist (12) ๐Ÿ—ƒ๏ธ Keyword Collector (140) โšก Prolific Year ยท Prolific ๐Ÿ’Ž Century Club ยท Bronze

Venues

ICML (9) NeurIPS (7) AAAI (6) ICLR (6) COLT (3) AISTATS (1)

Top co-authors

Joongkyu Lee (6) Taehyun Hwang (4) Harin Lee (4) Garud Iyengar (4) Gi-Soo Kim (3) Myunghee Cho Paik (3) Wonyoung Kim (2) Wooseong Cho (2) Seok-Jin Kim (2) Jung-hun Kim (2)

Research topics

Machine Learning (29) Optimization & Theory (21) Learning Types (18) Mathematics & Optimization (14) Optimization (14) Reinforcement Learning (12) Methods (12) Artificial Intelligence (5) Core AI (5) Deep Learning (4) Core Methods (4) Architectures (2) Computer Vision (1) Analysis (1) Application Areas (1)

Keywords

regret bound (23) contextual bandit (8) multi-armed bandit (4) thompson sampling (4) combinatorial bandit (3) linear bandit (3) upper confidence bound (3) reinforcement learning (3) multinomial logit (3) online learning (3) offline reinforcement learning (2) sublinear regret (2) uncertainty quantification (2) preference feedback (2) exploration bonus (2) function approximation (2) markov decision process (2) model-based reinforcement learning (2) assortment optimization (2) randomized exploration (2)

Papers

Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning COLT 2026 ADAM Optimization with Adaptive Batch Selection ICLR 2025 Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning ICLR 2025 Combinatorial Reinforcement Learning with Preference Feedback ICML 2025 Dynamic Assortment Selection and Pricing with Censored Preference Feedback ICLR 2025 Experimental Design for Semiparametric Bandits COLT 2025 Improved Online Confidence Bounds for Multinomial Logistic Bandits ICML 2025 Lasso Bandit with Compatibility Condition on Optimal Arm ICLR 2025 Linear Bandits with Partially Observable Features ICML 2025 Minimax Optimal Reinforcement Learning with Quasi-Optimism ICLR 2025 Optimal and Practical Batched Linear Bandit Algorithm ICML 2025 Symmetry-Aware GFlowNets ICML 2025 Demystifying Linear MDPs and Novel Dynamics Aggregation Framework ICLR 2024 Doubly Perturbed Task Free Continual Learning AAAI 2024 Follow-the-Perturbed-Leader with Frรฉchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds COLT 2024 Improved Regret of Linear Ensemble Sampling NeurIPS 2024 Learning Uncertainty-Aware Temporally-Extended Actions AAAI 2024 Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit NeurIPS 2024 Mixed-Effects Contextual Bandits AAAI 2024 Nearly Minimax Optimal Regret for Multinomial Logistic Bandit NeurIPS 2024 Queueing Matching Bandits with Preference Feedback NeurIPS 2024 Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation NeurIPS 2024 Cascading Contextual Assortment Bandits NeurIPS 2023 Combinatorial Neural Bandits ICML 2023 Model-based Offline Reinforcement Learning with Count-based Conservatism ICML 2023 Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation AAAI 2023 Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model ICML 2023 Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits AISTATS 2023 Multinomial Logit Contextual Bandits: Provable Optimality and Practicality AAAI 2021 Sparsity-Agnostic Lasso Bandit ICML 2021 Crowd Counting with Decomposed Uncertainty AAAI 2020 Thompson Sampling for Multinomial Logit Contextual Bandits NeurIPS 2019