Zobrazeno 1 - 10
of 4 760
pro vyhledávání: '"Steinke, P."'
We prove a linearization result for quasistatic fracture evolution in nonlinear elasticity. As the stiffness of the material tends to infinity, we show that rescaled displacement fields and their associated crack sets converge to a solution of quasis
Externí odkaz:
http://arxiv.org/abs/2411.13446
Autor:
Choquette-Choo, Christopher A., Ganesh, Arun, Haque, Saminul, Steinke, Thomas, Thakurta, Abhradeep
We study the problem of computing the privacy parameters for DP machine learning when using privacy amplification via random batching and noise correlated across rounds via a correlation matrix $\textbf{C}$ (i.e., the matrix mechanism). Past work on
Externí odkaz:
http://arxiv.org/abs/2410.06266
Autor:
Steinke, Thomas, Nasr, Milad, Ganesh, Arun, Balle, Borja, Choquette-Choo, Christopher A., Jagielski, Matthew, Hayes, Jamie, Thakurta, Abhradeep Guha, Smith, Adam, Terzis, Andreas
We propose a simple heuristic privacy analysis of noisy clipped stochastic gradient descent (DP-SGD) in the setting where only the last iterate is released and the intermediate iterates remain hidden. Namely, our heuristic assumes a linear structure
Externí odkaz:
http://arxiv.org/abs/2410.06186
Autor:
Hajikazemi, Sina, Steinke, Florian
Bilevel programming problems frequently arise in real-world applications across various fields, including transportation, economics, energy markets and healthcare. These problems have been proven to be NP-hard even in the simplest form with linear up
Externí odkaz:
http://arxiv.org/abs/2409.03619
Autor:
Watt, R., Kettle, B., Gerstmayr, E., King, B., Alejo, A., Astbury, S., Baird, C., Bohlen, S., Campbell, M., Colgan, C., Dannheim, D., Gregory, C., Harsh, H., Hatfield, P., Hinojosa, J., Hollatz, D., Katzir, Y., Morton, J., Murphy, C. D., Nurnberg, A., Osterhoff, J., Pérez-Callejo, G., Põder, K., Rajeev, P. P., Roedel, C., Roeder, F., Salgado, F. C., Samarin, G. M., Sarri, G., Seidel, A., Spindloe, C., Steinke, S., Streeter, M. J. V., Thomas, A. G. R., Underwood, C., Wu, W., Zepf, M., Rose, S. J., Mangles, S. P. D.
We report on a direct search for elastic photon-photon scattering using x-ray and $\gamma$ photons from a laser-plasma based experiment. A gamma photon beam produced by a laser wakefield accelerator provided a broadband gamma spectrum extending to ab
Externí odkaz:
http://arxiv.org/abs/2407.12915
Autor:
Braun, Martin, Gruhl, Christian, Hans, Christian A., Härtel, Philipp, Scholz, Christoph, Sick, Bernhard, Siefert, Malte, Steinke, Florian, Stursberg, Olaf, Berg, Sebastian Wende-von
Future energy systems are subject to various uncertain influences. As resilient systems they should maintain a constantly high operational performance whatever happens. We explore different levels and time scales of decision making in energy systems,
Externí odkaz:
http://arxiv.org/abs/2407.03021
Autor:
Gharaee, Zahra, Lowe, Scott C., Gong, ZeMing, Arias, Pablo Millan, Pellegrino, Nicholas, Wang, Austin T., Haurum, Joakim Bruslund, Zarubiieva, Iuliia, Kari, Lila, Steinke, Dirk, Taylor, Graham W., Fieguth, Paul, Chang, Angel X.
As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, this paper presents the BIOSCAN-5M Insect dataset to the machine learning community and establish several benchmark tasks. BIOSCAN-5M is a comprehensive dataset con
Externí odkaz:
http://arxiv.org/abs/2406.12723
In this paper, we study differentially private (DP) algorithms for computing the geometric median (GM) of a dataset: Given $n$ points, $x_1,\dots,x_n$ in $\mathbb{R}^d$, the goal is to find a point $\theta$ that minimizes the sum of the Euclidean dis
Externí odkaz:
http://arxiv.org/abs/2406.07407
We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the privacy parameters to arbitrary precision but must be carefully applied.
Externí odkaz:
http://arxiv.org/abs/2405.20769
Publikováno v:
IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 420-426, Jan. 2024
The precise point cloud ground segmentation is a crucial prerequisite of virtually all perception tasks for LiDAR sensors in autonomous vehicles. Especially the clustering and extraction of objects from a point cloud usually relies on an accurate rem
Externí odkaz:
http://arxiv.org/abs/2405.15664