Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Okada, Yukihiko"'
Estimation of conditional average treatment effects (CATEs) is an important topic in sciences. CATEs can be estimated with high accuracy if distributed data across multiple parties can be centralized. However, it is difficult to aggregate such data o
Externí odkaz:
http://arxiv.org/abs/2402.02672
Public memories of significant events shared within societies and groups have been conceptualized and studied as collective memory since the 1920s. Thanks to the recent advancement in digitization of public-domain knowledge and online user behaviors,
Externí odkaz:
http://arxiv.org/abs/2209.07033
Autor:
Imakura, Akira, Sakurai, Tetsuya, Okada, Yukihiko, Fujii, Tomoya, Sakamoto, Teppei, Abe, Hiroyuki
Multi-source data fusion, in which multiple data sources are jointly analyzed to obtain improved information, has considerable research attention. For the datasets of multiple medical institutions, data confidentiality and cross-institutional communi
Externí odkaz:
http://arxiv.org/abs/2208.14611
Recently, data collaboration (DC) analysis has been developed for privacy-preserving integrated analysis across multiple institutions. DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes i
Externí odkaz:
http://arxiv.org/abs/2208.12458
Publikováno v:
Expert Systems with Applications, 123024 (2023)
In recent years, the development of technologies for causal inference with privacy preservation of distributed data has gained considerable attention. Many existing methods for distributed data focus on resolving the lack of subjects (samples) and ca
Externí odkaz:
http://arxiv.org/abs/2208.07898
This study proposes a method for estimating the mechanical parameters of vehicles and bridges and the road unevenness, using only vehicle vibration and position data. In the proposed method, vehicle input and bridge vibration are estimated using rand
Externí odkaz:
http://arxiv.org/abs/2201.08014
Publikováno v:
In Expert Systems With Applications 15 June 2024 244
Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data. In this work, we explore an alternative federated learning system that enables integration of dime
Externí odkaz:
http://arxiv.org/abs/2011.06803
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. Analyzing distributed data is essential in many applicati
Externí odkaz:
http://arxiv.org/abs/2011.04437
Publikováno v:
In Expert Systems With Applications 15 October 2023 228