
Loss function based secondorder Jensen inequality and its application to particle variational inference
Bayesian model averaging, obtained as the expectation of a likelihood fu...
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Dynamic Hawkes Processes for Discovering Timeevolving Communities' States behind Diffusion Processes
Sequences of events including infectious disease outbreaks, social netwo...
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Fewshot Learning for Topic Modeling
Topic models have been successfully used for analyzing text documents. H...
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Metalearning representations for clustering with infinite Gaussian mixture models
For better clustering performance, appropriate representations are criti...
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Metalearning Oneclass Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection
Neural networkbased anomaly detection methods have shown to achieve hig...
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MetaLearning for Koopman Spectral Analysis with Short Timeseries
Koopman spectral analysis has attracted attention for nonlinear dynamica...
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Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
Adversarial training is actively studied for learning robust models agai...
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Neural Dynamic Mode Decomposition for EndtoEnd Modeling of Nonlinear Dynamics
Koopman spectral analysis has attracted attention for understanding nonl...
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Learning Contextualised Crosslingual Word Embeddings for Extremely LowResource Languages Using Parallel Corpora
We propose a new approach for learning contextualised crosslingual word...
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MetaActive Learning for Node Response Prediction in Graphs
Metalearning is an important approach to improve machine learning perfo...
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Fewshot Learning for Spatial Regression
We propose a fewshot learning method for spatial regression. Although G...
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Fewshot Learning for Timeseries Forecasting
Timeseries forecasting is important for many applications. Forecasting ...
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Gaussian Process Regression with Local Explanation
Gaussian process regression (GPR) is a fundamental model used in machine...
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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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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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Semisupervised Anomaly Detection on Attributed Graphs
We propose a simple yet effective method for detecting anomalous instanc...
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Efficient Transfer Bayesian Optimization with Auxiliary Information
We propose an efficient transfer Bayesian optimization method, which fin...
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Anomaly Detection with Inexact Labels
We propose a supervised anomaly detection method for data with inexact a...
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Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
We propose a probabilistic model for inferring the multivariate function...
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Deep Mixture Point Processes: Spatiotemporal Event Prediction with Rich Contextual Information
Predicting when and where events will occur in cities, like taxi pickup...
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Supervised Anomaly Detection based on Deep Autoregressive Density Estimators
We propose a supervised anomaly detection method based on neural density...
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Autoencoding Binary Classifiers for Supervised Anomaly Detection
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised...
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Finding Appropriate Traffic Regulations via Graph Convolutional Networks
Appropriate traffic regulations, e.g. planned road closure, are importan...
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Unsupervised Object Matching for Relational Data
We propose an unsupervised object matching method for relational data, w...
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Refining Coarsegrained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
We propose a probabilistic model for refining coarsegrained spatial dat...
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Variational Autoencoder with Implicit Optimal Priors
The variational autoencoder (VAE) is a powerful generative model that ca...
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Unsupervised Crosslingual Word Embedding by Multilingual Neural Language Models
We propose an unsupervised method to obtain crosslingual embeddings wit...
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Zeroshot Domain Adaptation without Domain Semantic Descriptors
We propose a method to infer domainspecific models such as classifiers ...
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Imitation networks: Fewshot learning of neural networks from scratch
In this paper, we propose imitation networks, a simple but effective met...
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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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Localized Lasso for HighDimensional Regression
We introduce the localized Lasso, which is suited for learning models th...
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Multiview Anomaly Detection via Probabilistic Latent Variable Models
We propose a nonparametric Bayesian probabilistic latent variable model ...
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Warped Mixtures for Nonparametric Cluster Shapes
A mixture of Gaussians fit to a single curved or heavytailed cluster wi...
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Tomoharu Iwata
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