39 citations · 50 across the 3 of their papers we have counts for
6 papers
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes
Maya Okawa, Tomoharu Iwata, Yusuke Tanaka +3
Sequences of events including infectious disease outbreaks, social network activities, and crimes are ubiquitous and the data on such events carry essential information about the u…
Few-shot Learning for Spatial Regression
Tomoharu Iwata, Yusuke Tanaka
We propose a few-shot learning method for spatial regression. Although Gaussian processes (GPs) have been successfully used for spatial regression, they require many observations i…
Probabilistic Optimal Transport based on Collective Graphical Models
Yasunori Akagi, Yusuke Tanaka, Tomoharu Iwata +2
Optimal Transport (OT) is being widely used in various fields such as machine learning and computer vision, as it is a powerful tool for measuring the similarity between probabilit…
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata +4
We propose a probabilistic model for inferring the multivariate function from multiple areal data sets with various granularities. Here, the areal data are observed not at location…
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information
Maya Okawa, Tomoharu Iwata, Takeshi Kurashima +3
Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields s…
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
Yusuke Tanaka, Tomoharu Iwata, Toshiyuki Tanaka +3
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the a…