Min-hwan Oh
31 papers · 2019–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (6) π Interdisciplinary Bridge π§ Keyword Pioneer π Renaissance Researcher (5) π Academic Marathon (6)
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Renaissance Researcher
(5)
π
Conference Polyglot
(6)
π
Keyword Champion
(3)
π
Grand Slam
ποΈ
Keyword Collector
(84)
π
Century Club
(31)
β‘
Prolific Year
(10)
Conferences
ICML (9)
NIPS (7)
AAAI (6)
ICLR (6)
COLT (2)
AISTATS (1)
Top co-authors
Keywords
regret bound
(15)
contextual bandit
(8)
multinomial logit
(3)
upper confidence bound
(3)
thompson sampling
(3)
online learning
(3)
linear contextual bandit
(2)
randomized exploration
(2)
neural network
(2)
uncertainty quantification
(2)
linear bandit
(2)
combinatorial bandit
(2)
markov decision process
(2)
multinomial logistic
(2)
function approximation
(2)
reinforcement learning
(2)
model-based reinforcement learning
(2)
assortment optimization
(2)
multi-armed bandit
(2)
exploration-exploitation tradeoff
(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