Tomoharu Iwata

research

∙ 07/23/2023

Information-theoretic Analysis of Test Data Sensitivity in Uncertainty

Bayesian inference is often utilized for uncertainty quantification task...

0 Futoshi Futami, et al. ∙

research

∙ 05/19/2023

Meta-learning for heterogeneous treatment effect estimation with closed-form solvers

This article proposes a meta-learning method for estimating the conditio...

0 Tomoharu Iwata, et al. ∙

research

∙ 12/26/2022

Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies

We propose a neural network-based model for nonlinear dynamics in contin...

0 Tomoharu Iwata, et al. ∙

research

∙ 11/02/2022

Linear Embedding-based High-dimensional Batch Bayesian Optimization without Reconstruction Mappings

The optimization of high-dimensional black-box functions is a challengin...

0 Shuhei A. Horiguchi, et al. ∙

research

∙ 10/04/2022

Active Learning for Regression with Aggregated Outputs

Due to the privacy protection or the difficulty of data collection, we c...

0 Tomoharu Iwata, et al. ∙

research

∙ 08/16/2022

Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces

We propose a data-driven method for controlling the frequency and conver...

0 Tomoharu Iwata, et al. ∙

research

∙ 07/07/2022

Predicting Opinion Dynamics via Sociologically-Informed Neural Networks

Opinion formation and propagation are crucial phenomena in social networ...

0 Maya Okawa, et al. ∙

research

∙ 06/24/2022

Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains

Aggregate data often appear in various fields such as socio-economics an...

0 Yusuke Tanaka, et al. ∙

research

∙ 06/20/2022

Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space

Many neural network-based out-of-distribution (OoD) detection methods ha...

0 Tomoharu Iwata, et al. ∙

research

∙ 06/02/2022

Excess risk analysis for epistemic uncertainty with application to variational inference

We analyze the epistemic uncertainty (EU) of supervised learning in Baye...

0 Futoshi Futami, et al. ∙

research

∙ 02/14/2022

Tight integration of neural- and clustering-based diarization through deep unfolding of infinite Gaussian mixture model

Speaker diarization has been investigated extensively as an important ce...

0 Keisuke Kinoshita, et al. ∙

research

∙ 12/07/2021

Training Deep Models to be Explained with Fewer Examples

Although deep models achieve high predictive performance, it is difficul...

0 Tomoharu Iwata, et al. ∙

research

∙ 11/26/2021

Evacuation Shelter Scheduling Problem

Evacuation shelters, which are urgently required during natural disaster...

0 Hitoshi Shimizu, et al. ∙

research

∙ 11/01/2021

End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization

For Bayesian optimization (BO) on high-dimensional data with complex str...

0 Tomoharu Iwata, et al. ∙

research

∙ 07/02/2021

Few-shot Learning for Unsupervised Feature Selection

We propose a few-shot learning method for unsupervised feature selection...

0 Atsutoshi Kumagai, et al. ∙

research

∙ 07/02/2021

Meta-Learning for Relative Density-Ratio Estimation

The ratio of two probability densities, called a density-ratio, is a vit...

0 Atsutoshi Kumagai, et al. ∙

research

∙ 06/29/2021

Meta-learning for Matrix Factorization without Shared Rows or Columns

We propose a method that meta-learns a knowledge on matrix factorization...

0 Tomoharu Iwata, et al. ∙

research

∙ 06/09/2021

Loss function based second-order Jensen inequality and its application to particle variational inference

Bayesian model averaging, obtained as the expectation of a likelihood fu...

0 Futoshi Futami, et al. ∙

research

∙ 05/24/2021

Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes

Sequences of events including infectious disease outbreaks, social netwo...

0 Maya Okawa, et al. ∙

research

∙ 04/19/2021

Few-shot Learning for Topic Modeling

Topic models have been successfully used for analyzing text documents. H...

0 Tomoharu Iwata, et al. ∙

research

∙ 03/01/2021

Meta-learning representations for clustering with infinite Gaussian mixture models

For better clustering performance, appropriate representations are criti...

0 Tomoharu Iwata, et al. ∙

research

∙ 03/01/2021

Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection

Neural network-based anomaly detection methods have shown to achieve hig...

0 Tomoharu Iwata, et al. ∙

research

∙ 02/09/2021

Meta-Learning for Koopman Spectral Analysis with Short Time-series

Koopman spectral analysis has attracted attention for nonlinear dynamica...

0 Tomoharu Iwata, et al. ∙

research

∙ 02/05/2021

Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression

Adversarial training is actively studied for learning robust models agai...

0 Masanori Yamada, et al. ∙

research

∙ 12/11/2020

Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics

Koopman spectral analysis has attracted attention for understanding nonl...

0 Tomoharu Iwata, et al. ∙

research

∙ 10/27/2020

Learning Contextualised Cross-lingual Word Embeddings for Extremely Low-Resource Languages Using Parallel Corpora

We propose a new approach for learning contextualised cross-lingual word...

0 Takashi Wada, et al. ∙

research

∙ 10/12/2020

Meta-Active Learning for Node Response Prediction in Graphs

Meta-learning is an important approach to improve machine learning perfo...

0 Tomoharu Iwata, et al. ∙

research

∙ 10/09/2020

Few-shot Learning for Spatial Regression

We propose a few-shot learning method for spatial regression. Although G...

0 Tomoharu Iwata, et al. ∙

research

∙ 09/30/2020

Few-shot Learning for Time-series Forecasting

Time-series forecasting is important for many applications. Forecasting ...

0 Tomoharu Iwata, et al. ∙

research

∙ 07/03/2020

Gaussian Process Regression with Local Explanation

Gaussian process regression (GPR) is a fundamental model used in machine...

0 Yuya Yoshikawa, et al. ∙

research

∙ 06/16/2020

Probabilistic Optimal Transport based on Collective Graphical Models

Optimal Transport (OT) is being widely used in various fields such as ma...

0 Yasunori Akagi, et al. ∙

research

∙ 03/13/2020

Neural Generators of Sparse Local Linear Models for Achieving both Accuracy and Interpretability

For reliability, it is important that the predictions made by machine le...

0 Yuya Yoshikawa, et al. ∙

research

∙ 02/27/2020

Semi-supervised Anomaly Detection on Attributed Graphs

We propose a simple yet effective method for detecting anomalous instanc...

0 Atsutoshi Kumagai, et al. ∙

research

∙ 09/17/2019

Efficient Transfer Bayesian Optimization with Auxiliary Information

We propose an efficient transfer Bayesian optimization method, which fin...

4 Tomoharu Iwata, et al. ∙

research

∙ 09/11/2019

Anomaly Detection with Inexact Labels

We propose a supervised anomaly detection method for data with inexact a...

0 Tomoharu Iwata, et al. ∙

research

∙ 07/19/2019

Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs

We propose a probabilistic model for inferring the multivariate function...

0 Yusuke Tanaka, et al. ∙

research

∙ 06/21/2019

Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information

Predicting when and where events will occur in cities, like taxi pick-up...

1 Maya Okawa, et al. ∙

research

∙ 04/12/2019

Supervised Anomaly Detection based on Deep Autoregressive Density Estimators

We propose a supervised anomaly detection method based on neural density...

0 Tomoharu Iwata, et al. ∙

research

∙ 03/26/2019

Autoencoding Binary Classifiers for Supervised Anomaly Detection

We propose the Autoencoding Binary Classifiers (ABC), a novel supervised...

0 Yuki Yamanaka, et al. ∙

research

∙ 10/23/2018

Finding Appropriate Traffic Regulations via Graph Convolutional Networks

Appropriate traffic regulations, e.g. planned road closure, are importan...

0 Tomoharu Iwata, et al. ∙

research

∙ 10/09/2018

Unsupervised Object Matching for Relational Data

We propose an unsupervised object matching method for relational data, w...

0 Tomoharu Iwata, et al. ∙

research

∙ 09/21/2018

Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities

We propose a probabilistic model for refining coarse-grained spatial dat...

0 Yusuke Tanaka, et al. ∙

research

∙ 09/14/2018

Variational Autoencoder with Implicit Optimal Priors

The variational autoencoder (VAE) is a powerful generative model that ca...

0 Hiroshi Takahashi, et al. ∙

research

∙ 09/07/2018

Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models

We propose an unsupervised method to obtain cross-lingual embeddings wit...

0 Takashi Wada, et al. ∙

research

∙ 07/09/2018

Zero-shot Domain Adaptation without Domain Semantic Descriptors

We propose a method to infer domain-specific models such as classifiers ...

0 Atsutoshi Kumagai, et al. ∙

research

∙ 02/08/2018

Imitation networks: Few-shot learning of neural networks from scratch

In this paper, we propose imitation networks, a simple but effective met...

0 Akisato Kimura, et al. ∙

research

∙ 07/19/2017

Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes

We propose a simple method that combines neural networks and Gaussian pr...

0 Tomoharu Iwata, et al. ∙

research

∙ 03/22/2016

Localized Lasso for High-Dimensional Regression

We introduce the localized Lasso, which is suited for learning models th...

0 Makoto Yamada, et al. ∙

research

∙ 11/13/2014

Multi-view Anomaly Detection via Probabilistic Latent Variable Models

We propose a nonparametric Bayesian probabilistic latent variable model ...

0 Tomoharu Iwata, et al. ∙

research

∙ 08/09/2014

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster wi...

0 Tomoharu Iwata, et al. ∙

Tomoharu Iwata

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