Search Results for author: Min-hwan Oh

Found 11 papers, 3 papers with code

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

no code implementations11 Jun 2022 Wonyoung Kim, Min-hwan Oh, Myunghee Cho Paik

We propose a novel algorithm for linear contextual bandits with $O(\sqrt{dT \log T})$ regret bound, where $d$ is the dimension of contexts and $T$ is the time horizon.

Multi-Armed Bandits

Personalized Federated Learning with Server-Side Information

1 code implementation23 May 2022 Jaehun Song, Min-hwan Oh, Hyung-Sin Kim

Personalized Federated Learning (FL) is an emerging research field in FL that learns an easily adaptable global model in the presence of data heterogeneity among clients.

Personalized Federated Learning

Multinomial Logit Contextual Bandits: Provable Optimality and Practicality

no code implementations25 Mar 2021 Min-hwan Oh, Garud Iyengar

We propose upper confidence bound based algorithms for this MNL contextual bandit.

Multi-Armed Bandits

Sparsity-Agnostic Lasso Bandit

no code implementations16 Jul 2020 Min-hwan Oh, Garud Iyengar, Assaf Zeevi

We consider a stochastic contextual bandit problem where the dimension $d$ of the feature vectors is potentially large, however, only a sparse subset of features of cardinality $s_0 \ll d$ affect the reward function.

Sequential Anomaly Detection using Inverse Reinforcement Learning

no code implementations22 Apr 2020 Min-hwan Oh, Garud Iyengar

In order to construct a reliable anomaly detection method and take into consideration the confidence of the predicted anomaly score, we adopt a Bayesian approach for IRL.

Anomaly Detection Decision Making +1

Counting and Segmenting Sorghum Heads

no code implementations30 May 2019 Min-hwan Oh, Peder Olsen, Karthikeyan Natesan Ramamurthy

We also propose a novel instance segmentation algorithm using the estimated density map, to identify the individual panicles in the presence of occlusion.

Crowd Counting Instance Segmentation +1

Crowd Counting with Decomposed Uncertainty

no code implementations15 Mar 2019 Min-hwan Oh, Peder A. Olsen, Karthikeyan Natesan Ramamurthy

Uncertainty quantification accompanied by point estimation can lead to a more informed decision, and even improve the prediction quality.

Crowd Counting

Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs

1 code implementation21 Dec 2018 Hiroki Kanezashi, Toyotaro Suzumura, Dario Garcia-Gasulla, Min-hwan Oh, Satoshi Matsuoka

We propose an incremental graph pattern matching algorithm to deal with time-evolving graph data and also propose an adaptive optimization system based on reinforcement learning to recompute vertices in the incremental process more efficiently.


Directed Exploration in PAC Model-Free Reinforcement Learning

no code implementations31 Aug 2018 Min-hwan Oh, Garud Iyengar

We study an exploration method for model-free RL that generalizes the counter-based exploration bonus methods and takes into account long term exploratory value of actions rather than a single step look-ahead.

Efficient Exploration Q-Learning +1

Graph Topological Features via GAN

no code implementations ICLR 2018 Weiyi Liu, Hal Cooper, Min-hwan Oh

Inspired by the success of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs.

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