39 citations · 128 across the 20 of their papers we have counts for
34 papers
Linear Embedding-based High-dimensional Batch Bayesian Optimization without Reconstruction Mappings
Shuhei A. Horiguchi, Tomoharu Iwata, Taku Tsuzuki +1
The optimization of high-dimensional black-box functions is a challenging problem. When a low-dimensional linear embedding structure can be assumed, existing Bayesian optimization…
Active Learning for Regression with Aggregated Outputs
Tomoharu Iwata
Due to the privacy protection or the difficulty of data collection, we cannot observe individual outputs for each instance, but we can observe aggregated outputs that are summed ov…
Tight integration of neural- and clustering-based diarization through deep unfolding of infinite Gaussian mixture model
Keisuke Kinoshita, Marc Delcroix, Tomoharu Iwata
Speaker diarization has been investigated extensively as an important central task for meeting analysis. Recent trend shows that integration of end-to-end neural (EEND)-and cluster…
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization
Tomoharu Iwata
For Bayesian optimization (BO) on high-dimensional data with complex structure, neural network-based kernels for Gaussian processes (GPs) have been used to learn flexible surrogate…
Few-shot Learning for Unsupervised Feature Selection
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
We propose a few-shot learning method for unsupervised feature selection, which is a task to select a subset of relevant features in unlabeled data. Existing methods usually requir…
Meta-Learning for Relative Density-Ratio Estimation
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
The ratio of two probability densities, called a density-ratio, is a vital quantity in machine learning. In particular, a relative density-ratio, which is a bounded extension of th…