Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Sebastian Pokutta"'
Autor:
Sébastien Designolle, Gabriele Iommazzo, Mathieu Besançon, Sebastian Knebel, Patrick Gelß, Sebastian Pokutta
Publikováno v:
Physical Review Research, Vol 5, Iss 4, p 043059 (2023)
In Bell scenarios with two outcomes per party, we algorithmically consider the two sides of the membership problem for the local polytope: Constructing local models and deriving separating hyperplanes, that is, Bell inequalities. We take advantage of
Externí odkaz:
https://doaj.org/article/146401fedf464cf2b4064a0b77d73dd7
Autor:
Tabea Kossen, Manuel A. Hirzel, Vince I. Madai, Franziska Boenisch, Anja Hennemuth, Kristian Hildebrand, Sebastian Pokutta, Kartikey Sharma, Adam Hilbert, Jan Sobesky, Ivana Galinovic, Ahmed A. Khalil, Jochen B. Fiebach, Dietmar Frey
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been show
Externí odkaz:
https://doaj.org/article/427bb9acfb874fcfad4b7c1e7629490b
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-17 (2018)
Abstract We characterize the performance of sequential information-guided sensing (Info-Greedy Sensing) when the model parameters (means and covariance matrices) are estimated and inaccurate. Our theoretical results focus on Gaussian signals and esta
Externí odkaz:
https://doaj.org/article/aefaccd512f74408a8da96dc2e3d9f5b
Publikováno v:
INFORMS Journal on Computing. 34:2611-2620
We present FrankWolfe.jl, an open-source implementation of several popular Frank–Wolfe and conditional gradients variants for first-order constrained optimization. The package is designed with flexibility and high performance in mind, allowing for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74a49925258ae1c0a5f9249784b751ab
https://doi.org/10.1287/ijoc.2022.1191
https://doi.org/10.1287/ijoc.2022.1191
Autor:
Kevin-Martin Aigner, Andreas Bärmann, Kristin Braun, Frauke Liers, Sebastian Pokutta, Oskar Schneider, Kartikey Sharma, Sebastian Tschuppik
Publikováno v:
INFORMS Journal on Optimization.
Stochastic optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters. Because the latter is often unknown, distributionally robust optimiz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4e8214ee7459783724fc49f3b4dd7e9
https://doi.org/10.1287/ijoo.2023.0091
https://doi.org/10.1287/ijoo.2023.0091
Publikováno v:
Journal of Optimization Theory and Applications. 192:799-829
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f740755a4fea428536805e153902b44
https://doi.org/10.1007/s10957-021-01989-7
https://doi.org/10.1007/s10957-021-01989-7
Publikováno v:
Integration of Constraint Programming, Artificial Intelligence, and Operations Research ISBN: 9783031332708
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6c64b364d28fc9e1b07335f0f7b2540
https://doi.org/10.1007/978-3-031-33271-5_8
https://doi.org/10.1007/978-3-031-33271-5_8
Publikováno v:
Mathematical Programming. 197:191-214
The approximate Caratheodory theorem states that given a compact convex set $${\mathcal {C}}\subset {\mathbb {R}}^n$$ and $$p\in [2,+\infty [$$ , each point $$x^*\in {\mathcal {C}}$$ can be approximated to $$\epsilon $$ -accuracy in the $$\ell _p$$ -
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::010de66d04dc251d3a48b9064affde5f
https://doi.org/10.1007/s10107-021-01735-x
https://doi.org/10.1007/s10107-021-01735-x
Publikováno v:
Operations Research Letters. 49:565-571
The Frank-Wolfe algorithm is a method for constrained optimization relying on linear minimizations, as opposed to projections. Therefore, a motivation put forward in a large body of work on the Frank-Wolfe algorithm is the computational advantage of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4cbe643749941308b5cb469ac3aa5b60
https://doi.org/10.1016/j.orl.2021.06.005
https://doi.org/10.1016/j.orl.2021.06.005
Autor:
Sebastian Pokutta
Publikováno v:
Mitteilungen der Deutschen Mathematiker-Vereinigung. 28:213-219
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e842ae0c44b0156854632b0c11f9454
https://doi.org/10.1515/dmvm-2020-0067
https://doi.org/10.1515/dmvm-2020-0067