Search Results for author:
Found 23 papers, 7 papers with code
Estimation of conditional average treatment effects on distributed data: A privacy-preserving approach
Second, our method enables collaborative estimation between different parties as well as multiple time points because the dimensionality-reduced intermediate representations can be accumulated.
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
Notably, the optimization techniques, namely black-box VI and natural-gradient VI, can be reinterpreted as specific instances of the proposed Wasserstein gradient descent.
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings
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.
Moreau-Yoshida Variational Transport: A General Framework For Solving Regularized Distributional Optimization Problems
We consider a general optimization problem of minimizing a composite objective functional defined over a class of probability distributions.
Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration
Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use.
Inferring cell-specific lncRNA regulation with single-cell RNA-sequencing data in the developing human neocortex
However, to analyze lncRNA regulation regarding individual cells, we focus on single-cell RNA-sequencing (scRNA-seq) data instead of bulk data.
Unveiling interpretable development-specific gene signatures in the developing human prefrontal cortex with ICGS
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.
Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives
We compare the proposed method with PushGP, as well as a method using subprograms manually extracted by a human.
Non-readily identifiable data collaboration analysis for multiple datasets including personal information
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
DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes integrated analysis via collaboration representations without sharing the original data.
Collaborative causal inference on distributed data
Many existing methods for distributed data focus on resolving the lack of subjects (samples) and can only reduce random errors in estimating treatment effects.
A Particle-Based Algorithm for Distributional Optimization on \textit{Constrained Domains} via Variational Transport and Mirror Descent
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
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
In this paper, we propose a divide-and-conquer based large-scale spectral clustering method to strike a good balance between efficiency and effectiveness.
Ranked #2 on
Image/Document Clustering
on pendigits
Accuracy and Privacy Evaluations of Collaborative Data Analysis
Distributed data analysis without revealing the individual data has recently attracted significant attention in several applications.
Ensemble Learning for Spectral Clustering
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.
Ranked #1 on
Image/Document Clustering
on Wine
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data.
Interpretable collaborative data analysis on distributed data
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.
Multiclass spectral feature scaling method for dimensionality reduction
Irregular features disrupt the desired classification.
Data collaboration analysis for distributed datasets
In this paper, we propose a data collaboration analysis method for distributed datasets.
Classification of X-Ray Protein Crystallization Using Deep Convolutional Neural Networks with a Finder Module
We compared our crystallization image recognition method with a high precision method using Inception-V3.
Spectral feature scaling method for supervised dimensionality reduction
Spectral dimensionality reduction methods enable linear separations of complex data with high-dimensional features in a reduced space.
Alternating optimization method based on nonnegative matrix factorizations for deep neural networks
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).
