45 citations · 367 across the 61 of their papers we have counts for
6 papers · 1 filter
Efficient Transfer Bayesian Optimization with Auxiliary Information
Tomoharu Iwata, Takuma Otsuka
We propose an efficient transfer Bayesian optimization method, which finds the maximum of an expensive-to-evaluate black-box function by using data on related optimization tasks. O…
Anomaly Detection with Inexact Labels
Tomoharu Iwata, Machiko Toyoda, Shotaro Tora +1
We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instan…
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…
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators
Tomoharu Iwata, Yuki Yamanaka
We propose a supervised anomaly detection method based on neural density estimators, where the negative log likelihood is used for the anomaly score. Density estimators have been w…
Autoencoding Binary Classifiers for Supervised Anomaly Detection
Yuki Yamanaka, Tomoharu Iwata, Hiroshi Takahashi +2
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervi…