Min-hwan Oh

According to our database, Min-hwan Oh authored at least 42 papers between 2015 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Optimal and Practical Batched Linear Bandit Algorithm.

Sanghoon Yu, Min-hwan Oh

CoRR, July, 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, July, 2025

Symmetry-Aware GFlowNets.

Hohyun Kim, Seunggeun Lee, Min-hwan Oh

CoRR, June, 2025

Combinatorial Reinforcement Learning with Preference Feedback.

Joongkyu Lee, Min-hwan Oh

CoRR, February, 2025

Improved Online Confidence Bounds for Multinomial Logistic Bandits.

Joongkyu Lee, Min-hwan Oh

CoRR, February, 2025

Linear Bandits with Partially Observable Features.

Wonyoung Kim, Sungwoo Park, Garud Iyengar, Assaf Zeevi, Min-hwan Oh

CoRR, February, 2025

Minimax Optimal Reinforcement Learning with Quasi-Optimism.

Harin Lee, Min-hwan Oh

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Lasso Bandit with Compatibility Condition on Optimal Arm.

Harin Lee, Taehyun Hwang, Min-hwan Oh

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Dynamic Assortment Selection and Pricing with Censored Preference Feedback.

Jung-hun Kim, Min-hwan Oh

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

ADAM Optimization with Adaptive Batch Selection.

Gyu-Yeol Kim, Min-hwan Oh

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning.

Hyungkyu Kang, Min-hwan Oh

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Experimental Design for Semiparametric Bandits.

Seok-Jin Kim, Gi-Soo Kim, Min-hwan Oh

Proceedings of the Thirty Eighth Annual Conference on Learning Theory, 2025

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

Nearly Minimax Optimal Regret for Multinomial Logistic Bandit.

Joongkyu Lee, Min-hwan Oh

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Improved Regret of Linear Ensemble Sampling.

Harin Lee, Min-hwan Oh

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit.

Seok-Jin Kim, Min-hwan Oh

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Queueing Matching Bandits with Preference Feedback.

Jung-hun Kim, Min-hwan Oh

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation.

Wooseong Cho, Taehyun Hwang, Joongkyu Lee, Min-hwan Oh

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Demystifying Linear MDPs and Novel Dynamics Aggregation Framework.

Joongkyu Lee, Min-hwan Oh

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Learning Uncertainty-Aware Temporally-Extended Actions.

Joongkyu Lee, Seung Joon Park, Yunhao Tang, Min-hwan Oh

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Mixed-Effects Contextual Bandits.

Kyungbok Lee, Myunghee Cho Paik, Min-hwan Oh, Gi-Soo Kim

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Doubly Perturbed Task Free Continual Learning.

Byung Hyun Lee, Min-hwan Oh, Se Young Chun

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Cascading Contextual Assortment Bandits.

Hyun-Jun Choi, Rajan Udwani, Min-hwan Oh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Model-based Offline Reinforcement Learning with Count-based Conservatism.

Byeongchan Kim, Min-hwan Oh

Proceedings of the International Conference on Machine Learning, 2023

Combinatorial Neural Bandits.

Taehyun Hwang, Kyuwook Chai, Min-hwan Oh

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits.

Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation.

Taehyun Hwang, Min-hwan Oh

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Personalized Federated Learning With Server-Side Information.

Jaehun Song, Min Hwan Oh, Hyung-Sin Kim

IEEE Access, 2022

Stochastic-Expert Variational Autoencoder for Collaborative Filtering.

Yoon-Sik Cho, Min-hwan Oh

Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

2021

Sparsity-Agnostic Lasso Bandit.

Min-hwan Oh, Garud Iyengar, Assaf Zeevi

Proceedings of the 38th International Conference on Machine Learning, 2021

Multinomial Logit Contextual Bandits: Provable Optimality and Practicality.

Min-hwan Oh, Garud Iyengar

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Crowd Counting with Decomposed Uncertainty.

Min-hwan Oh, Peder A. Olsen, Karthikeyan Natesan Ramamurthy

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Counting and Segmenting Sorghum Heads.

Min-hwan Oh, Peder A. Olsen, Karthikeyan Natesan Ramamurthy

CoRR, 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

Thompson Sampling for Multinomial Logit Contextual Bandits.

Min-hwan Oh, Garud Iyengar

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Sequential Anomaly Detection using Inverse Reinforcement Learning.

Min-hwan Oh, Garud Iyengar

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

2018

Directed Exploration in PAC Model-Free Reinforcement Learning.

Min-hwan Oh, Garud Iyengar

CoRR, 2018

Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs.

Hiroki Kanezashi, Toyotaro Suzumura, Dario Garcia-Gasulla, Min-hwan Oh, Satoshi Matsuoka

Proceedings of the 25th IEEE International Conference on High Performance Computing, 2018

2017

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

2015

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