Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Ziteng Sun"'
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 10, Iss 2 (2020)
We develop differentially private methods for estimating various distributional properties. Given a sample from a discrete distribution p, some functional f, and accuracy and privacy parameters alpha and epsilon, the goal is to estimate f(p) up to ac
Externí odkaz:
https://doaj.org/article/8423f407cda248c9a041eb9b0996ce0e
Publikováno v:
Applied Sciences, Vol 12, Iss 2, p 754 (2022)
This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In or
Externí odkaz:
https://doaj.org/article/a005331dbd82428f911071f38539a1bb
Publikováno v:
International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab0678bee0ac9837c637055ce817da92
https://doi.org/10.1117/12.2681273
https://doi.org/10.1117/12.2681273
Publikováno v:
ISIT
We study the role of interactivity in distributed statistical inference under information constraints, e.g., communication constraints and local differential privacy. We focus on the tasks of goodness-of-fit testing and estimation of discrete distrib
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::631e90e7c2c6287709bdb6b3dc1457c5
https://doi.org/10.1109/tit.2021.3123905
https://doi.org/10.1109/tit.2021.3123905
Publikováno v:
Ocean Engineering. 266:112509
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5fbe1698e5cb2ed71c9f6249f5c6196f
https://doi.org/10.1016/j.oceaneng.2022.112509
https://doi.org/10.1016/j.oceaneng.2022.112509
We study goodness-of-fit and independence testing of discrete distributions in a setting where samples are distributed across multiple users. The users wish to preserve the privacy of their data while enabling a central server to perform the tests. U
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c845c61cfb2c069777176dbebdf82570
http://arxiv.org/abs/2101.07981
http://arxiv.org/abs/2101.07981
Autor:
Lie He, Sebastian U. Stich, Mariana Raykova, Phillip B. Gibbons, Mehryar Mohri, David Evans, Badih Ghazi, Felix X. Yu, Sen Zhao, Jianyu Wang, Zheng Xu, Weikang Song, Prateek Mittal, Ramesh Raskar, Zachary Garrett, Farinaz Koushanfar, H. Brendan McMahan, Ayfer Ozgur, Mikhail Khodak, Rafael G. L. D'Oliveira, Jakub Konecní, Aurélien Bellet, Arjun Nitin Bhagoji, Hubert Eichner, Han Yu, Adrià Gascón, Ananda Theertha Suresh, Sanmi Koyejo, Praneeth Vepakomma, Josh Gardner, Chaoyang He, Florian Tramèr, Tancrède Lepoint, Salim El Rouayheb, Peter Kairouz, Li Xiong, Kallista Bonawitz, Rasmus Pagh, Tara Javidi, Mehdi Bennis, Dawn Song, Martin Jaggi, Zhouyuan Huo, Hang Qi, Gauri Joshi, Qiang Yang, Richard Nock, Yang Liu, Brendan Avent, Justin Hsu, Rachel Cummings, Graham Cormode, Marco Gruteser, Aleksandra Korolova, Ziteng Sun, Zaid Harchaoui, Ben Hutchinson, Zachary Charles, Daniel Ramage
Publikováno v:
Foundations and Trends in Machine Learning
Foundations and Trends in Machine Learning, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, Now Publishers, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, Now Publishers, 2021, 14 (1-2), pp.1-210
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b1ccc10027ba1ce68ce0210510e8bdc
https://inria.hal.science/hal-02406503v2/document
https://inria.hal.science/hal-02406503v2/document
Autor:
Dongmei Li, Sourbh Bhadane, Victor Hernandez Martinez, Grace Deng, Jayadev Acharya, Aaron B. Wagner, Peter Wu, David S. Matteson, Elaine L. Hill, Ajay Anand, Sean Ryan, Ziteng Sun
Publikováno v:
Stat
Stat (International Statistical Institute)
Stat (International Statistical Institute)
Social distancing measures have been imposed across the US in order to stem the spread of COVID‐19. We quantify the reduction in doubling rate, by state, that is associated with this intervention. Using the earlier of K‐12 school closures and res
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b01abb1ad1bb4c678a72a7bdb320426c
Autor:
Grace Deng, Sean Ryan, Dongmei Li, Peter Wu, Victor Hernandez Martinez, Jayadev Acharya, Sourbh Bhadane, Ajay Anand, Elaine L. Hill, Ziteng Sun, Aaron B. Wagner, David S. Matteson
Social distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19. In this work we analyze the effect of current social distancing measures in the United States. We quantify th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08476234774ee4e5ed9a53f512bcbdfa
https://doi.org/10.1101/2020.04.27.20081836
https://doi.org/10.1101/2020.04.27.20081836
Autor:
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawit, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's