14 citations · 25 across the 5 of their papers we have counts for
7 papers
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…
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…