Search Results for author: Tetsuya Sakurai

Found 23 papers, 7 papers with code

Estimation of conditional average treatment effects on distributed data: A privacy-preserving approach

no code implementations5 Feb 2024 Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai

Second, our method enables collaborative estimation between different parties as well as multiple time points because the dimensionality-reduced intermediate representations can be accumulated.

Privacy Preserving

Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference

no code implementations25 Oct 2023 Dai Hai Nguyen, Tetsuya Sakurai, Hiroshi Mamitsuka

Notably, the optimization techniques, namely black-box VI and natural-gradient VI, can be reinterpreted as specific instances of the proposed Wasserstein gradient descent.

Variational Inference

Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings

no code implementations1 Aug 2023 Akihiro Mizoguchi, Anna Bogdanova, Akira Imakura, Tetsuya Sakurai

However, federated learning is encumbered by low accuracy in not identically and independently distributed (non-IID) settings, i. e., data partitioning has a large label bias, and is considered unsuitable for compound datasets, which tend to have large label bias.

Federated Learning

Moreau-Yoshida Variational Transport: A General Framework For Solving Regularized Distributional Optimization Problems

1 code implementation31 Jul 2023 Dai Hai Nguyen, Tetsuya Sakurai

We consider a general optimization problem of minimizing a composite objective functional defined over a class of probability distributions.

Bayesian Inference

Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration

no code implementations6 Dec 2022 Anna Bogdanova, Akira Imakura, Tetsuya Sakurai, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe

Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use.

Privacy Preserving

Unveiling interpretable development-specific gene signatures in the developing human prefrontal cortex with ICGS

1 code implementation15 Nov 2022 Meng Huang, Xiucai Ye, Tetsuya Sakurai

In this paper, to unveil interpretable development-specific gene signatures in human PFC, we propose a novel gene selection method, named Interpretable Causality Gene Selection (ICGS), which adopts a Bayesian Network (BN) to represent causality between multiple gene variables and a development variable.

Contrastive Learning

Inferring cell-specific lncRNA regulation with single-cell RNA-sequencing data in the developing human neocortex

1 code implementation15 Nov 2022 Meng Huang, Jiangtao Ma, Changzhou Long, Junpeng Zhang, Xiucai Ye, Tetsuya Sakurai

However, to analyze lncRNA regulation regarding individual cells, we focus on single-cell RNA-sequencing (scRNA-seq) data instead of bulk data.

Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives

1 code implementation8 Sep 2022 Yifan He, Claus Aranha, Tetsuya Sakurai

We compare the proposed method with PushGP, as well as a method using subprograms manually extracted by a human.

Program Synthesis

Non-readily identifiable data collaboration analysis for multiple datasets including personal information

no code implementations31 Aug 2022 Akira Imakura, Tetsuya Sakurai, Yukihiko Okada, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe

This study then proposes a non-readily identifiable DC analysis only sharing non-readily identifiable data for multiple medical datasets including personal information.

Another Use of SMOTE for Interpretable Data Collaboration Analysis

no code implementations26 Aug 2022 Akira Imakura, Masateru Kihira, Yukihiko Okada, Tetsuya Sakurai

DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes integrated analysis via collaboration representations without sharing the original data.

feature selection Privacy Preserving

Collaborative causal inference on distributed data

no code implementations16 Aug 2022 Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai

Many existing methods for distributed data focus on resolving the lack of subjects (samples) and can only reduce random errors in estimating treatment effects.

Causal Inference Dimensionality Reduction

A Particle-Based Algorithm for Distributional Optimization on \textit{Constrained Domains} via Variational Transport and Mirror Descent

no code implementations1 Aug 2022 Dai Hai Nguyen, Tetsuya Sakurai

We consider the optimization problem of minimizing an objective functional, which admits a variational form and is defined over probability distributions on the constrained domain, which poses challenges to both theoretical analysis and algorithmic design.

LSEC: Large-scale spectral ensemble clustering

1 code implementation18 Jun 2021 Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai

In LSEC, a large-scale spectral clustering based efficient ensemble generation framework is designed to generate various base clusterings within a low computational complexity.


Divide-and-conquer based Large-Scale Spectral Clustering

1 code implementation30 Apr 2021 Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai

In this paper, we propose a divide-and-conquer based large-scale spectral clustering method to strike a good balance between efficiency and effectiveness.

Clustering Image/Document Clustering

Accuracy and Privacy Evaluations of Collaborative Data Analysis

no code implementations27 Jan 2021 Akira Imakura, Anna Bogdanova, Takaya Yamazoe, Kazumasa Omote, Tetsuya Sakurai

Distributed data analysis without revealing the individual data has recently attracted significant attention in several applications.

Dimensionality Reduction Federated Learning

Ensemble Learning for Spectral Clustering

1 code implementation20 Nov 2020 Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai

Instead of directly using the clustering results obtained from each base spectral clustering algorithm, the proposed method learns a robust presentation of graph Laplacian by ensemble learning from the spectral embedding of each base spectral clustering algorithm.

Clustering Ensemble Learning +1

Interpretable collaborative data analysis on distributed data

no code implementations9 Nov 2020 Akira Imakura, Hiroaki Inaba, Yukihiko Okada, Tetsuya Sakurai

This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distributed data.

Federated Learning

Data collaboration analysis for distributed datasets

no code implementations20 Feb 2019 Akira Imakura, Tetsuya Sakurai

In this paper, we propose a data collaboration analysis method for distributed datasets.

Privacy Preserving

Spectral feature scaling method for supervised dimensionality reduction

no code implementations18 May 2018 Momo Matsuda, Keiichi Morikuni, Tetsuya Sakurai

Spectral dimensionality reduction methods enable linear separations of complex data with high-dimensional features in a reduced space.

Clustering General Classification +1

Alternating optimization method based on nonnegative matrix factorizations for deep neural networks

no code implementations16 May 2016 Tetsuya Sakurai, Akira Imakura, Yuto Inoue, Yasunori Futamura

In this paper, we propose a novel approach for computing weight matrices of fully-connected DNNs by using two types of semi-nonnegative matrix factorizations (semi-NMFs).

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