18 citations · 54 across the 14 of their papers we have counts for
15 papers
Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation
Wonyoung Kim, Min-Hwan Oh, Garud Iyengar +1
Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad applicability. While existing studi…
Model-based Offline Reinforcement Learning with Count-based Conservatism
Byeongchan Kim, Min-hwan Oh
In this paper, we propose a model-based offline reinforcement learning method that integrates count-based conservatism, named . Our method utilizes the count e…
Combinatorial Neural Bandits
Taehyun Hwang, Kyuwook Chai, Min-hwan Oh
We consider a contextual combinatorial bandit problem where in each round a learning agent selects a subset of arms and receives feedback on the selected arms according to their sc…
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
Taehyun Hwang, Min-hwan Oh
We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is parametrized by an unknown transition core with featur…
Personalized Federated Learning with Server-Side Information
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. However,…
Multinomial Logit Contextual Bandits: Provable Optimality and Practicality
Min-hwan Oh, Garud Iyengar
We consider a sequential assortment selection problem where the user choice is given by a multinomial logit (MNL) choice model whose parameters are unknown. In each period, the lea…
Sparsity-Agnostic Lasso Bandit
Min-hwan Oh, Garud Iyengar, Assaf Zeevi
We consider a stochastic contextual bandit problem where the dimension of the feature vectors is potentially large, however, only a sparse subset of features of cardinality $s_…
Sequential Anomaly Detection using Inverse Reinforcement Learning
Min-hwan Oh, Garud Iyengar
One of the most interesting application scenarios in anomaly detection is when sequential data are targeted. For example, in a safety-critical environment, it is crucial to have an…
Counting and Segmenting Sorghum Heads
Min-hwan Oh, Peder Olsen, Karthikeyan Natesan Ramamurthy
Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate…
Crowd Counting with Decomposed Uncertainty
Min-hwan Oh, Peder A. Olsen, Karthikeyan Natesan Ramamurthy
Research in neural networks in the field of computer vision has achieved remarkable accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed. U…
Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs
Hiroki Kanezashi, Toyotaro Suzumura, Dario Garcia-Gasulla +2
Graph pattern matching algorithms to handle million-scale dynamic graphs are widely used in many applications such as social network analytics and suspicious transaction detections…
Directed Exploration in PAC Model-Free Reinforcement Learning
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 t…
Learning Graph Topological Features via GAN
Weiyi Liu, Hal Cooper, Min Hwan Oh +4
Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological…
Can GAN Learn Topological Features of a Graph?
Weiyi Liu, Pin-Yu Chen, Hal Cooper +3
This paper is first-line research expanding GANs into graph topology analysis. By leveraging the hierarchical connectivity structure of a graph, we have demonstrated that generativ…
A shotgun sampling solution for the common input problem in neural connectivity inference
Daniel Soudry, Suraj Keshri, Patrick Stinson +3
Inferring connectivity in neuronal networks remains a key challenge in statistical neuroscience. The `common input' problem presents the major roadblock: it is difficult to reliabl…