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 Comput. Biol., 2015)

Can GAN Learn Topological Features of a Graph? - Weiyi Liu; Pin-Yu Chen; Hal James Cooper; Min Hwan Oh; Sailung Yeung; Toyotaro Suzumura (CoRR, 2017)

Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs. - Hiroki Kanezashi; Toyotaro Suzumura; Dario Garcia-Gasulla; Min-hwan Oh; Satoshi Matsuoka (HiPC, 2018)

Directed Exploration in PAC Model-Free Reinforcement Learning. - Min-hwan Oh; Garud Iyengar (CoRR, 2018)

Sequential Anomaly Detection using Inverse Reinforcement Learning. - Min-hwan Oh; Garud Iyengar (KDD, 2019)

Thompson Sampling for Multinomial Logit Contextual Bandits. - Min-hwan Oh; Garud Iyengar (NeurIPS, 2019)

Corrections to "Learning Graph Topological Features via GAN". - Weiyi Liu; Pin-Yu Chen; Fucai Yu; Toyotaro Suzumura; Guangmin Hu; Hal James Cooper; Min-hwan Oh; Sailung Yeung (IEEE Access, 2019)

Counting and Segmenting Sorghum Heads. - Min-hwan Oh; Peder A. Olsen; Karthikeyan Natesan Ramamurthy (CoRR, 2019)

Crowd Counting with Decomposed Uncertainty. - Min-hwan Oh; Peder A. Olsen; Karthikeyan Natesan Ramamurthy (AAAI, 2020)

Multinomial Logit Contextual Bandits: Provable Optimality and Practicality. - Min-hwan Oh; Garud Iyengar (AAAI, 2021)

Sparsity-Agnostic Lasso Bandit. - Min-hwan Oh; Garud Iyengar; Assaf Zeevi (ICML, 2021)

Stochastic-Expert Variational Autoencoder for Collaborative Filtering. - Yoon-Sik Cho; Min-hwan Oh (WWW, 2022)

Personalized Federated Learning With Server-Side Information. - Jaehun Song; Min Hwan Oh; Hyung-Sin Kim (IEEE Access, 2022)

Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation. - Taehyun Hwang; Min-hwan Oh (AAAI, 2023)

Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits. - Wonyoung Kim; Myunghee Cho Paik; Min-hwan Oh (AISTATS, 2023)

Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model. - Young-Geun Choi; Gi-Soo Kim; Yunseo Choi; Wooseong Cho; Myunghee Cho Paik; Min-hwan Oh (ICML, 2023)

Combinatorial Neural Bandits. - Taehyun Hwang; Kyuwook Chai; Min-hwan Oh (ICML, 2023)

Model-based Offline Reinforcement Learning with Count-based Conservatism. - Byeongchan Kim; Min-hwan Oh (ICML, 2023)

Cascading Contextual Assortment Bandits. - Hyun-Jun Choi; Rajan Udwani; Min-hwan Oh (NeurIPS, 2023)

Doubly Perturbed Task Free Continual Learning. - Byung Hyun Lee; Min-hwan Oh; Se Young Chun (AAAI, 2024)

Mixed-Effects Contextual Bandits. - Kyungbok Lee; Myunghee Cho Paik; Min-hwan Oh; Gi-Soo Kim (AAAI, 2024)

Learning Uncertainty-Aware Temporally-Extended Actions. - Joongkyu Lee; Seung Joon Park; Yunhao Tang; Min-hwan Oh (AAAI, 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 (COLT, 2024)

Demystifying Linear MDPs and Novel Dynamics Aggregation Framework. - Joongkyu Lee; Min-hwan Oh (ICLR, 2024)

Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation. - Wooseong Cho; Taehyun Hwang; Joongkyu Lee; Min-hwan Oh (NeurIPS, 2024)

Queueing Matching Bandits with Preference Feedback. - Jung-hun Kim; Min-hwan Oh (NeurIPS, 2024)

Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit. - Seok-Jin Kim; Min-hwan Oh (NeurIPS, 2024)

Improved Regret of Linear Ensemble Sampling. - Harin Lee; Min-hwan Oh (NeurIPS, 2024)

Nearly Minimax Optimal Regret for Multinomial Logistic Bandit. - Joongkyu Lee; Min-hwan Oh (NeurIPS, 2024)

Magnituder Layers for Implicit Neural Representations in 3D. - Sang Min Kim; Byeongchan Kim; Arijit Sehanobish; Krzysztof Choromanski; Dongseok Shim; Avinava Dubey; Min-hwan Oh (CoRR, 2024)

Experimental Design for Semiparametric Bandits. - Seok-Jin Kim; Gi-Soo Kim; Min-hwan Oh (COLT, 2025)

Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning. - Hyungkyu Kang; Min-hwan Oh (ICLR, 2025)

ADAM Optimization with Adaptive Batch Selection. - Gyu-Yeol Kim; Min-hwan Oh (ICLR, 2025)

Dynamic Assortment Selection and Pricing with Censored Preference Feedback. - Jung-hun Kim; Min-hwan Oh (ICLR, 2025)

Lasso Bandit with Compatibility Condition on Optimal Arm. - Harin Lee; Taehyun Hwang; Min-hwan Oh (ICLR, 2025)

Minimax Optimal Reinforcement Learning with Quasi-Optimism. - Harin Lee; Min-hwan Oh (ICLR, 2025)

Linear Bandits with Partially Observable Features. - Wonyoung Kim; Sungwoo Park; Garud Iyengar; Assaf Zeevi; Min-hwan Oh (CoRR, 2025)

Improved Online Confidence Bounds for Multinomial Logistic Bandits. - Joongkyu Lee; Min-hwan Oh (CoRR, 2025)

Combinatorial Reinforcement Learning with Preference Feedback. - Joongkyu Lee; Min-hwan Oh (CoRR, 2025)

Symmetry-Aware GFlowNets. - Hohyun Kim; Seunggeun Lee; Min-hwan Oh (CoRR, 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 (CoRR, 2025)

Optimal and Practical Batched Linear Bandit Algorithm. - Sanghoon Yu; Min-hwan Oh (CoRR, 2025)