45 citations · 367 across the 61 of their papers we have counts for
42 papers · 1 filter
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +1
Semi-supervised anomaly detection, which aims to improve the anomaly detection performance by using a small amount of labeled anomaly data in addition to unlabeled data, has attrac…
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs
Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata +1
The operator learning has received significant attention in recent years, with the aim of learning a mapping between function spaces. Prior works have proposed deep neural networks…
Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation
Tomoharu Iwata, Atsutoshi Kumagai
Although Gaussian processes (GPs) with deep kernels have been successfully used for meta-learning in regression tasks, its uncertainty estimation performance can be poor. We propos…
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs
Tomoharu Iwata, Yusuke Tanaka, Naonori Ueda
We propose a neural network-based meta-learning method to efficiently solve partial differential equation (PDE) problems. The proposed method is designed to meta-learn how to solve…
Information-theoretic Analysis of Test Data Sensitivity in Uncertainty
Futoshi Futami, Tomoharu Iwata
Bayesian inference is often utilized for uncertainty quantification tasks. A recent analysis by Xu and Raginsky 2022 rigorously decomposed the predictive uncertainty in Bayesian in…
Meta-learning for heterogeneous treatment effect estimation with closed-form solvers
Tomoharu Iwata, Yoichi Chikahara
This article proposes a meta-learning method for estimating the conditional average treatment effect (CATE) from a few observational data. The proposed method learns how to estimat…
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies
Tomoharu Iwata, Yoshinobu Kawahara
We propose a neural network-based model for nonlinear dynamics in continuous time that can impose inductive biases on decay rates and/or frequencies. Inductive biases are helpful f…
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…
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…
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space
Tomoharu Iwata, Atsutoshi Kumagai
Many neural network-based out-of-distribution (OoD) detection methods have been proposed. However, they require many training data for each target task. We propose a simple yet eff…
Excess risk analysis for epistemic uncertainty with application to variational inference
Futoshi Futami, Tomoharu Iwata, Naonori Ueda +2
Bayesian deep learning plays an important role especially for its ability evaluating epistemic uncertainty (EU). Due to computational complexity issues, approximation methods such…
Training Deep Models to be Explained with Fewer Examples
Tomoharu Iwata, Yuya Yoshikawa
Although deep models achieve high predictive performance, it is difficult for humans to understand the predictions they made. Explainability is important for real-world application…
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…
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…
Meta-learning for Matrix Factorization without Shared Rows or Columns
Tomoharu Iwata
We propose a method that meta-learns a knowledge on matrix factorization from various matrices, and uses the knowledge for factorizing unseen matrices. The proposed method uses a n…
Loss function based second-order Jensen inequality and its application to particle variational inference
Futoshi Futami, Tomoharu Iwata, Naonori Ueda +2
Bayesian model averaging, obtained as the expectation of a likelihood function by a posterior distribution, has been widely used for prediction, evaluation of uncertainty, and mode…
Meta-learning representations for clustering with infinite Gaussian mixture models
Tomoharu Iwata
For better clustering performance, appropriate representations are critical. Although many neural network-based metric learning methods have been proposed, they do not directly tra…
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection
Tomoharu Iwata, Atsutoshi Kumagai
Neural network-based anomaly detection methods have shown to achieve high performance. However, they require a large amount of training data for each task. We propose a neural netw…
Meta-Learning for Koopman Spectral Analysis with Short Time-series
Tomoharu Iwata, Yoshinobu Kawahara
Koopman spectral analysis has attracted attention for nonlinear dynamical systems since we can analyze nonlinear dynamics with a linear regime by embedding data into a Koopman spac…
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
Masanori Yamada, Sekitoshi Kanai, Tomoharu Iwata +4
Adversarial training is actively studied for learning robust models against adversarial examples. A recent study finds that adversarially trained models degenerate generalization p…
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics
Tomoharu Iwata, Yoshinobu Kawahara
Koopman spectral analysis has attracted attention for understanding nonlinear dynamical systems by which we can analyze nonlinear dynamics with a linear regime by lifting observati…
Meta-Active Learning for Node Response Prediction in Graphs
Tomoharu Iwata
Meta-learning is an important approach to improve machine learning performance with a limited number of observations for target tasks. However, when observations are unbalancedly o…
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…