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07/23/2023
Information-theoretic Analysis of Test Data Sensitivity in Uncertainty
Bayesian inference is often utilized for uncertainty quantification task...
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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...
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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...
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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...
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10/04/2022
Active Learning for Regression with Aggregated Outputs
Due to the privacy protection or the difficulty of data collection, we c...
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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...
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07/07/2022
Predicting Opinion Dynamics via Sociologically-Informed Neural Networks
Opinion formation and propagation are crucial phenomena in social networ...
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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...
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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...
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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...
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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...
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12/07/2021
Training Deep Models to be Explained with Fewer Examples
Although deep models achieve high predictive performance, it is difficul...
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11/26/2021
Evacuation Shelter Scheduling Problem
Evacuation shelters, which are urgently required during natural disaster...
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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...
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07/02/2021
Few-shot Learning for Unsupervised Feature Selection
We propose a few-shot learning method for unsupervised feature selection...
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07/02/2021
Meta-Learning for Relative Density-Ratio Estimation
The ratio of two probability densities, called a density-ratio, is a vit...
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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...
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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...
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05/24/2021
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes
Sequences of events including infectious disease outbreaks, social netwo...
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04/19/2021
Few-shot Learning for Topic Modeling
Topic models have been successfully used for analyzing text documents. H...
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03/01/2021
Meta-learning representations for clustering with infinite Gaussian mixture models
For better clustering performance, appropriate representations are criti...
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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...
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02/09/2021
Meta-Learning for Koopman Spectral Analysis with Short Time-series
Koopman spectral analysis has attracted attention for nonlinear dynamica...
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02/05/2021
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
Adversarial training is actively studied for learning robust models agai...
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12/11/2020
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics
Koopman spectral analysis has attracted attention for understanding nonl...
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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...
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10/12/2020
Meta-Active Learning for Node Response Prediction in Graphs
Meta-learning is an important approach to improve machine learning perfo...
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10/09/2020
Few-shot Learning for Spatial Regression
We propose a few-shot learning method for spatial regression. Although G...
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09/30/2020
Few-shot Learning for Time-series Forecasting
Time-series forecasting is important for many applications. Forecasting ...
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07/03/2020
Gaussian Process Regression with Local Explanation
Gaussian process regression (GPR) is a fundamental model used in machine...
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06/16/2020
Probabilistic Optimal Transport based on Collective Graphical Models
Optimal Transport (OT) is being widely used in various fields such as ma...
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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...
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02/27/2020
Semi-supervised Anomaly Detection on Attributed Graphs
We propose a simple yet effective method for detecting anomalous instanc...
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09/17/2019
Efficient Transfer Bayesian Optimization with Auxiliary Information
We propose an efficient transfer Bayesian optimization method, which fin...
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09/11/2019
Anomaly Detection with Inexact Labels
We propose a supervised anomaly detection method for data with inexact a...
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07/19/2019
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
We propose a probabilistic model for inferring the multivariate function...
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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...
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04/12/2019
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators
We propose a supervised anomaly detection method based on neural density...
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03/26/2019
Autoencoding Binary Classifiers for Supervised Anomaly Detection
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised...
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10/23/2018
Finding Appropriate Traffic Regulations via Graph Convolutional Networks
Appropriate traffic regulations, e.g. planned road closure, are importan...
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10/09/2018
Unsupervised Object Matching for Relational Data
We propose an unsupervised object matching method for relational data, w...
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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...
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09/14/2018
Variational Autoencoder with Implicit Optimal Priors
The variational autoencoder (VAE) is a powerful generative model that ca...
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09/07/2018
Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models
We propose an unsupervised method to obtain cross-lingual embeddings wit...
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07/09/2018
Zero-shot Domain Adaptation without Domain Semantic Descriptors
We propose a method to infer domain-specific models such as classifiers ...
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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...
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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...
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03/22/2016
Localized Lasso for High-Dimensional Regression
We introduce the localized Lasso, which is suited for learning models th...
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11/13/2014
Multi-view Anomaly Detection via Probabilistic Latent Variable Models
We propose a nonparametric Bayesian probabilistic latent variable model ...
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08/09/2014
