Differentially Private Bayesian Inference for Generalized Linear Models

Tejas Kulkarni; Joonas Jälkö; Antti Koskela; Samuel Kaski; Antti Honkela

Differentially Private Bayesian Inference for Generalized Linear Models

Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Original language	English
Title of host publication	Proceedings of the 38th International Conference on Machine Learning, PMLR
Editors	M Meila, T Zhang
Number of pages	12
Publisher	Journal of Machine Learning Research
Publication date	2021
Pages	5838-5849
Publication status	Published - 2021
MoE publication type	A4 Article in conference proceedings
Event	International Conference on Machine Learning (ICML) - Duration: 18 Jul 2021 → 24 Jul 2021

Publication series

Name	Proceedings of Machine Learning Research
Publisher	JMLR-JOURNAL MACHINE LEARNING RESEARCH
Volume	139
ISSN (Print)	2640-3498

Fields of Science

113 Computer and information sciences

Access to Document

kulkarni21aFinal published version, 517 KBLicence: Unspecified

http://proceedings.mlr.press/v139/kulkarni21a.htmlLicence: Unspecified

Cite this

Kulkarni, T., Jälkö, J., Koskela, A., Kaski, S., & Honkela, A. (2021). Differentially Private Bayesian Inference for Generalized Linear Models. In M. Meila, & T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning, PMLR (pp. 5838-5849). (Proceedings of Machine Learning Research; Vol. 139). Journal of Machine Learning Research. http://proceedings.mlr.press/v139/kulkarni21a.html

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title = "Differentially Private Bayesian Inference for Generalized Linear Models",

keywords = "113 Computer and information sciences",

author = "Tejas Kulkarni and Joonas J{\"a}lk{\"o} and Antti Koskela and Samuel Kaski and Antti Honkela",

year = "2021",

language = "English",

series = "Proceedings of Machine Learning Research",

publisher = "Journal of Machine Learning Research",

pages = "5838--5849",

editor = "M Meila and T Zhang",

booktitle = "Proceedings of the 38th International Conference on Machine Learning, PMLR",

address = "United States",

note = "International Conference on Machine Learning (ICML) ; Conference date: 18-07-2021 Through 24-07-2021",

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Kulkarni, T, Jälkö, J , Koskela, A , Kaski, S & Honkela, A 2021, Differentially Private Bayesian Inference for Generalized Linear Models. in M Meila & T Zhang (eds), Proceedings of the 38th International Conference on Machine Learning, PMLR. Proceedings of Machine Learning Research, vol. 139, Journal of Machine Learning Research, pp. 5838-5849, International Conference on Machine Learning (ICML), 18/07/2021. <http://proceedings.mlr.press/v139/kulkarni21a.html>

Differentially Private Bayesian Inference for Generalized Linear Models. / Kulkarni, Tejas; Jälkö, Joonas ; Koskela, Antti et al.
Proceedings of the 38th International Conference on Machine Learning, PMLR. ed. / M Meila; T Zhang. Journal of Machine Learning Research, 2021. p. 5838-5849 (Proceedings of Machine Learning Research; Vol. 139).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Differentially Private Bayesian Inference for Generalized Linear Models

AU - Kulkarni, Tejas

AU - Jälkö, Joonas

AU - Koskela, Antti

AU - Kaski, Samuel

AU - Honkela, Antti

PY - 2021

Y1 - 2021

KW - 113 Computer and information sciences

M3 - Conference contribution

T3 - Proceedings of Machine Learning Research

SP - 5838

EP - 5849

BT - Proceedings of the 38th International Conference on Machine Learning, PMLR

A2 - Meila, M

A2 - Zhang, T

PB - Journal of Machine Learning Research

T2 - International Conference on Machine Learning (ICML)

Y2 - 18 July 2021 through 24 July 2021

ER -