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pro vyhledávání: '"Koskela, A."'
In this paper, we study the relationship between Sobolev extension domains and homogeneous Sobolev extension domains. Precisely, we obtain the following results. 1- Let $1\leq q\leq p\leq \infty$. Then a bounded $(L^{1, p}, L^{1, q})$-extension domai
Externí odkaz:
http://arxiv.org/abs/2411.11470
Autor:
Crossley, Alastair, Habermann, Karen, Horton, Emma, Koskela, Jere, Kyprianou, Andreas E., Osman, Sarah
Proton beam radiotherapy stands at the forefront of precision cancer treatment, leveraging the unique physical interactions of proton beams with human tissue to deliver minimal dose upon entry and deposit the therapeutic dose precisely at the so-call
Externí odkaz:
http://arxiv.org/abs/2409.06965
Autor:
Koskela, Pekka, Mishra, Riddhi
We show that the volume of the boundary of a bounded Sobolev $(p,q)$-extension domain is zero when $1\leq q
Externí odkaz:
http://arxiv.org/abs/2409.01170
Each homeomorphic parametrization of a Jordan curve via the unit circle extends to a homeomorphism of the entire plane. It is a natural question to ask if such a homeomorphism can be chosen so as to have some Sobolev regularity. This prompts the simp
Externí odkaz:
http://arxiv.org/abs/2408.00506
Autor:
Koskela, Antti
The hidden state threat model of differential privacy (DP) assumes that the adversary has access only to the final trained machine learning (ML) model, without seeing intermediate states during training. Current privacy analyses under this model, how
Externí odkaz:
http://arxiv.org/abs/2407.04884
We show that genealogical trees arising from a broad class of non-neutral models of population evolution converge to the Kingman coalescent under a suitable rescaling of time. As well as non-neutral biological evolution, our results apply to genetic
Externí odkaz:
http://arxiv.org/abs/2406.16465
Autor:
Koskela, Antti, Mohammadi, Jafar
We present a novel method for accurately auditing the differential privacy (DP) guarantees of DP mechanisms. In particular, our solution is applicable to auditing DP guarantees of machine learning (ML) models. Previous auditing methods tightly captur
Externí odkaz:
http://arxiv.org/abs/2406.04827
Private selection mechanisms (e.g., Report Noisy Max, Sparse Vector) are fundamental primitives of differentially private (DP) data analysis with wide applications to private query release, voting, and hyperparameter tuning. Recent work (Liu and Talw
Externí odkaz:
http://arxiv.org/abs/2402.06701
In the arena of privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) has outstripped the objective perturbation mechanism in popularity and interest. Though unrivaled in versatility, DP-SGD requires a non-t
Externí odkaz:
http://arxiv.org/abs/2401.00583
Autor:
Koskela, Pekka, Zhu, Zheng
For a continuous function $f:\mathbb{R}\to\mathbb{R}$, define the corresponding graph by setting \[\Gamma_f := {(x1, f(x1)) : x_1\in\mathbb{R}} .\] In this paper, we study the Sobolev extension property for the upper and lower domains over the graph
Externí odkaz:
http://arxiv.org/abs/2312.09497