DistriBayes: A Distributed Platform for Learning, Inference and Attribution on Large Scale Bayesian Network-Reference-Cited by-同舟云学术

DistriBayes: A Distributed Platform for Learning, Inference and Attribution on Large Scale Bayesian Network

Published:2023-02-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
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Author:

Ding Yi¹^ORCID,Zhou Jun²^ORCID,Cui Qing¹^ORCID,Wang Lin¹^ORCID,Zhang Mengqi¹^ORCID,Dong Yang¹^ORCID

Affiliation:

1. Ant Group, Beijing, China

2. Zhejiang University & Ant Group, Hangzhou, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539597.3573028

Reference9 articles.

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2. Jeffrey Dean , Greg S. Corrado , Rajat Monga , Kai Chen , Matthieu Devin , Quoc V. Le , Mark Z. Mao , Marc'Aurelio Ranzato , Andrew Senior , Paul Tucker , Ke Yang , and Andrew Y . Ng . 2012 . Large Scale Distributed Deep Networks. In NIPS. Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Andrew Y. Ng. 2012. Large Scale Distributed Deep Networks. In NIPS.

3. Fast Approximate Score Computation on Large-Scale Distributed Data for Learning Multinomial Bayesian Networks

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5. Microsoft Research. 2019. EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation. https://github.com/microsoft/EconML. Version 0.x. Microsoft Research. 2019. EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation. https://github.com/microsoft/EconML. Version 0.x.