Min-hwan Oh

According to our database, Min-hwan Oh authored at least 31 papers between 2015 and 2024.

Collaborative distances:

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

2024

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

Seok-Jin Kim, Min-hwan Oh

CoRR, 2024

Improved Regret of Linear Ensemble Sampling.

Harin Lee, Min-hwan Oh

CoRR, 2024

Queueing Matching Bandits with Preference Feedback.

Jung-hun Kim, Min-hwan Oh

CoRR, 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

Lasso Bandit with Compatibility Condition on Optimal Arm.

Harin Lee, Taehyun Hwang, Min-hwan Oh

CoRR, 2024

Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation.

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

CoRR, 2024

Nearly Minimax Optimal Regret for Multinomial Logistic Bandit.

Joongkyu Lee, Min-hwan Oh

CoRR, 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