Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Sebastian U. Stich"'
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:
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
Autor:
Yurii Nesterov, Sebastian U. Stich
Publikováno v:
SIAM Journal on Optimization. 27:110-123
In this paper we prove a new complexity bound for a variant of the accelerated coordinate descent method [Yu. Nesterov, SIAM J. Optim., 22 (2012), pp. 341--362]. We show that this method often outperforms the standard fast gradient methods (FGM [Yu.
Autor:
Sebastian U. Stich, József Solymosi, Radoslav Fulek, Hossein Nassajian Mojarrad, May Szedlák, Márton Naszódi
Let $P$ be a finite point set in the plane. A \emph{$c$-ordinary triangle} in $P$ is a subset of $P$ consisting of three non-collinear points such that each of the three lines determined by the three points contains at most $c$ points of $P$. Motivat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a19b80b72c8d7b1e6a4d15c5c945e5b0
http://arxiv.org/abs/1701.08183
http://arxiv.org/abs/1701.08183
Publikováno v:
SIAM Journal on Optimization. 23:1284-1309
We consider unconstrained randomized optimization of convex objective functions. We analyze the Random Pursuit algorithm, which iteratively computes an approximate solution to the optimization problem by repeated optimization over a randomly chosen o
Publikováno v:
Tyagi, H, Stich, S U & Gärtner, B 2016, ' On Two Continuum Armed Bandit Problems in High Dimensions ', Theory of Computing Systems, vol. 58, no. 1, pp. 191-222 . https://doi.org/10.1007/s00224-014-9570-8
We consider the problem of continuum armed bandits where the arms are indexed by a compact subset of ?d$\mathbb {R}^{d}$. For large d, it is well known that mere smoothness assumptions on the reward functions lead to regret bounds that suffer from th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f316b4ecb8f520c991ab5b12940f94f6
https://hdl.handle.net/20.500.11820/7b5bed32-0918-40f1-b68c-10dacbb05921
https://hdl.handle.net/20.500.11820/7b5bed32-0918-40f1-b68c-10dacbb05921
Autor:
Sebastian U. Stich
Publikováno v:
Parallel Problem Solving from Nature – PPSN XIII ISBN: 9783319107615
PPSN
PPSN
Recently it was shown by Nesterov (2011) that techniques form convex optimization can be used to successfully accelerate simple derivative-free randomized optimization methods. The appeal of those schemes lies in their low complexity, which is only
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6fc35c793c952a654eca2c28c4f7b470
https://doi.org/10.1007/978-3-319-10762-2_13
https://doi.org/10.1007/978-3-319-10762-2_13
We consider unconstrained randomized optimization of smooth convex objective functions in the gradient-free setting. We analyze Random Pursuit (RP) algorithms with fixed (F-RP) and variable metric (V-RP). The algorithms only use zeroth-order informat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5be5e6387c3a358d5e95e4ccfa19b43d
http://arxiv.org/abs/1210.5114
http://arxiv.org/abs/1210.5114
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642329364
PPSN (1)
PPSN (1)
We evaluate the performance of several gradient-free variable-metric continuous optimization schemes on a specific set of quadratic functions. We revisit a randomized Hessian approximation scheme (D. Leventhal and A. S. Lewis. Randomized Hessian esti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4fb955b4f42d90c033011546f4932a4
Autor:
Sebastian U. Stich, Dan Hefetz
Publikováno v:
The Electronic Journal of Combinatorics. 16
We consider the fair Hamiltonian cycle Maker-Breaker game, played on the edge set of the complete graph $K_n$ on $n$ vertices. It is known that Maker wins this game if $n$ is sufficiently large. We are interested in the minimum number of moves needed