42 citations · 111 across the 11 of their papers we have counts for
6 papers · 1 filter
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata +4
Aggregate data often appear in various fields such as socio-economics and public security. The aggregate data are associated not with points but with supports (e.g., spatial region…
Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm
Yasunori Akagi, Naoki Marumo, Hideaki Kim +2
The importance of aggregated count data, which is calculated from the data of multiple individuals, continues to increase. Collective Graphical Model (CGM) is a probabilistic appro…
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…