Linear Contextual Bandits with Quasi-Optimism
Min-hwan Oh, Harin Lee
Neural Information Processing Systems (NeurIPS), 2026
Linear Ensemble Sampling with Smaller Ensembles
Taehyun Hwang, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2026
Bilinear Matching Bandits
Wooseong Cho, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2026
Sharper Regret Bounds for Shampoo
Dahngoon Kim, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2026
Stochastic Matching Bandits with Rare Optimization Updates
Jung-hun Kim, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2026
Block Sphere Vector Quantization
Heesang Ann, Joongkyu Lee, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2026
Topic-Aware Contextual Cascading Bandits
Hyunjun Choi, Taehyun Hwang, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2026
Block Optimism for Nonstationary Bandits with Latent Linear Dynamics
Taehyun Hwang, Hyunjun Choi, Heesang Ann, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2026
Variance-Adaptive Optimal Algorithm for Reinforcement Learning with MNL Function Approximation
Wonyoung Kim, Garud Iyengar, Assaf Zeevi, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2026
Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards
Deokgyu Yoon, Hyungkyu Kang, Joongkyu Lee, Byeongchan Kim, Gyungin Shin, Sungrae Park, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2026
RelFlexformer: Efficient Attention Transformers for Integrable Relative Positional Encodings
Byeongchan Kim, Arijit Sehanobish, Kumar Avinava Dubey, Min-hwan Oh, Krzysztof Choromanski
Neural Information Processing Systems (NeurIPS), 2026
SNACK: A Sequential Notation Framework for Probabilistic Graph Generation
Hohyun Kim, Hyesung Kim, Min-hwan Oh, Seunggeun Lee
Neural Information Processing Systems (NeurIPS), 2026
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
Sanghoon Yu, Min-hwan Oh
International Conference on Machine Learning (ICML), 2026 (Spotlight)
Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
Joongkyu Lee, Min-hwan Oh
International Conference on Machine Learning (ICML), 2026
Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
Chaiwon Kim, Jongyeong Lee, Min-hwan Oh
International Conference on Machine Learning (ICML), 2026
Bilinear Bandits with Partially Observable Features
Wooseong Cho, Ji Hyeong Park, Min-hwan Oh
International Conference on Machine Learning (ICML), 2026
Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
Hyungkyu Kang, Byeongchan Kim, Min-hwan Oh
International Conference on Machine Learning (ICML), 2026
Generalized Linear Bandits with Memory
Heesang Ann, Hyunjun Choi, Taehyun Hwang, Younghoon Shin, Haeju Cheong, Min-hwan Oh
International Conference on Machine Learning (ICML), 2026
Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
Harin Lee, Min-hwan Oh
Conference on Learning Theory (COLT), 2026
Convergence of Muon with Newton-Schulz
Gyu Yeol Kim, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2026
Peng’s Q(λ) for Conservative Value Estimation in Offline Reinforcement Learning
Byeongchan Kim, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2026
Offline Preference-Based Value Optimization
Hyungkyu Kang, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2026
Diversified Multinomial Logit Contextual Bandits
Heesang Ann, Taehyun Hwang, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2026
Exploration via Feature Perturbation in Contextual Bandits
Seouh-won Yi, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2025 (Spotlight)
Infrequent Exploration in Linear Bandits
Harin Lee, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2025
Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
Joongkyu Lee, Seouh-won Yi, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2025
Tractable Multinomial Logit Contextual Bandits with Non-Linear Utilities
Taehyun Hwang, Dahngoon Kim, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2025
Thompson Sampling for Multi-Objective Linear Contextual Bandit
Somangchan Park, Heesang Ann, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2025
True Impact of Cascade Length in Contextual Cascading Bandits
Hyunjun Choi, Joongkyu Lee, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2025
Oracle-Efficient Combinatorial Semi-Bandits
Jung-hun Kim, Milan Vojnovic, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2025
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
Jongyeong Lee, Junya Honda, Shinji Ito, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2025
EUGens: Efficient, Unified and General Dense Layers
Sang Min Kim, Byeongchan Kim, Arijit Sehanobish, Somnath Basu Roy Chowdhury, Rahul Kidambi, Dongseok Shim, Avinava Dubey, Snigdha Chaturvedi, Min-hwan Oh, Krzysztof Choromanski
Neural Information Processing Systems (NeurIPS), 2025
AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
Eunsu Baek, Keondo Park, Jeonggil Ko, Min-hwan Oh, Taesik Gong, Hyung-Sin Kim
Neural Information Processing Systems (NeurIPS), Position Paper Track, 2025
Improved Online Confidence Bounds for Multinomial Logistic Bandits
Joongkyu Lee, Min-hwan Oh
International Conference on Machine Learning (ICML), 2025
Combinatorial Reinforcement Learning with Preference Feedback
Joongkyu Lee, Min-hwan Oh
International Conference on Machine Learning (ICML), 2025
Optimal and Practical Batched Linear Bandit Algorithm
Sanghoon Yu, Min-hwan Oh
International Conference on Machine Learning (ICML), 2025
Symmetry-Aware GFlowNets
Hohyun Kim, Seunggeun Lee, Min-hwan Oh
International Conference on Machine Learning (ICML), 2025
Linear Bandits with Partially Observable Features
Wonyoung Kim, Sungwoo Park, Garud Iyengar, Assaf Zeevi, Min-hwan Oh
International Conference on Machine Learning (ICML), 2025
Experimental Design for Semiparametric Bandits
Seok-Jin Kim, Gi-Soo Kim, Min-hwan Oh
Conference on Learning Theory (COLT), 2025
Minimax Optimal Reinforcement Learning with Quasi-Optimism
Harin Lee, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2025
ADAM Optimization with Adaptive Batch Selection
Gyu Yeol Kim, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2025
Dynamic Assortment Selection and Pricing with Censored Preference Feedback
Jung-hun Kim, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2025
Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning
Hyungkyu Kang, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2025
Lasso Bandit with Compatibility Condition on Optimal Arm
Harin Lee, Taehyun Hwang, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2025
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
Joongkyu Lee, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2024
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
Wooseong Cho, Taehyun Hwang, Joongkyu Lee, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2024
Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
Seok-Jin Kim, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2024
Queueing Matching Bandits with Preference Feedback
Jung-hun Kim, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2024
Improved Regret of Linear Ensemble Sampling
Harin Lee, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2024
Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds
Jongyeong Lee, Junya Honda, Shinji Ito, Min-hwan Oh
Conference on Learning Theory (COLT), 2024
Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
Joongkyu Lee, Min-hwan Oh
International Conference on Learning Representations (ICLR), 2024
Learning Uncertainty-Aware Temporally-Extended Actions
Joongkyu Lee, Seung Joon Park, Yunhao Tang, Min-hwan Oh
AAAI Conference on Artificial Intelligence (AAAI), 2024
Mixed-Effects Contextual Bandits
Kyungbok Lee, Myunghee Cho Paik, Min-hwan Oh, Gi-Soo Kim
AAAI Conference on Artificial Intelligence (AAAI), 2024
Doubly Perturbed Task Free Continual Learning
Byung Hyun Lee, Min-hwan Oh, Se Young Chun
AAAI Conference on Artificial Intelligence (AAAI), 2024
Cascading Contextual Assortment Bandits
Hyunjun Choi, Rajan Udwani, Min-hwan Oh
Neural Information Processing Systems (NeurIPS), 2023
Combinatorial Neural Bandits
Taehyun Hwang, Kyuwook Chai, Min-hwan Oh
International Conference on Machine Learning (ICML), 2023
Model-based Offline Reinforcement Learning with Count-based Conservatism
Byeongchan Kim, Min-hwan Oh
International Conference on Machine Learning (ICML), 2023
Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model
Young-Geun Choi, Gi-Soo Kim, Yunseo Choi, Wooseong Cho, Myunghee C. Paik, Min-hwan Oh
International Conference on Machine Learning (ICML), 2023
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits
Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Model-based Reinforcement Learning with Multinomial Logistic Function Approximation
Taehyun Hwang, Min-hwan Oh
AAAI Conference on Artificial Intelligence (AAAI), 2023
Stochastic-Expert Variational Autoencoder for Collaborative Filtering
Yoon-Sik Cho, Min-hwan Oh
The ACM Web Conference (WWW), 2022
Sparsity-Agnostic Lasso Bandit
Min-hwan Oh, Garud Iyengar, Assaf Zeevi
International Conference on Machine Learning (ICML), 2021
– INFORMS Applied Probability Society Student Paper Award Finalist
– Journal version: Operations Research (accepted, to appear)
Multinomial Logit Contextual Bandits: Provable Optimality and Practicality
Min-hwan Oh, Garud Iyengar
AAAI Conference on Artificial Intelligence (AAAI), 2021
Crowd Counting with Decomposed Uncertainty
Min-hwan Oh, Peder A. Olsen, Karthikeyan N. Ramamurthy
AAAI Conference on Artificial Intelligence (AAAI), 2020
Thompson Sampling for Multinomial Logit Contextual Bandits
Min-hwan Oh, Garud Iyengar
Neural Information Processing Systems (NeurIPS), 2019
Sequential Anomaly Detection using Inverse Reinforcement Learning
Min-hwan Oh, Garud Iyengar
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2019
– Oral presentation in research paper track (top 9% of total submissions)
Automatic Event Detection in Basketball using Hidden Markov Models with Energy based Defensive Assignment
Suraj Keshri, Min-hwan Oh, Sheng Zhang, Garud Iyengar
Journal of Quantitative Analysis in Sports. 15.2: 141-153. 2019
Learning Graph Topological Features via GAN
Weiyi Liu, Hal Cooper, Min-Hwan Oh, Pin-Yu Chen, Sailung Yeung, Fucai Yu, Toyotaro Suzumura, Guangmin Hu
IEEE Access, 2019
– Preliminary version appeared at Workshop on Implicit Models, International Conference on Machine Learning (ICML), 2017
Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs
Hiroki Kanezashi, Toyotaro Suzumura, Dario Garcia-Gasulla, Min-hwan Oh, Satoshi Matsuoka
IEEE International Conference on High Performance Computing, Data, and Analytics, 2018
– Best Paper Award Winner
Efficient “Shotgun” Inference of Neural Connectivity from Highly Sub-sampled Activity Data
Daniel Soudry, Suraj Keshri, Patrick Stinson, Min-hwan Oh, Garud Iyengar, Liam Paninski
PLoS Computational Biology, 11 (10), 2015
Graphical Model for Basketball Match Simulation
Min-hwan Oh, Suraj Keshri, Garud Iyengar
MIT Sloan Sports Analytics Conference, 2015
– Finalist in Research Paper Competition (top 2% of total submissions)