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Min-hwan Oh

31 papers · 2019–2025 · 6 conferences · across top CS/AI conferences

Achievements

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+7 more ↓ 🌍 Conference Polyglot (6) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌈 Renaissance Researcher (5) πŸƒ Academic Marathon (6)
🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (6) πŸ† Keyword Champion (3) πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (84) πŸ’Ž Century Club (31) ⚑ Prolific Year (10)

Conferences

ICML (9) NIPS (7) AAAI (6) ICLR (6) COLT (2) AISTATS (1)

Papers

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