Zobrazeno 1 - 10
of 117
pro vyhledávání: '"Kulkarni, Tejas"'
Autor:
Koskela, Antti, Kulkarni, Tejas
Publikováno v:
NeurIPS 2023
Tuning the hyperparameters of differentially private (DP) machine learning (ML) algorithms often requires use of sensitive data and this may leak private information via hyperparameter values. Recently, Papernot and Steinke (2022) proposed a certain
Externí odkaz:
http://arxiv.org/abs/2301.11989
In recent years, local differential privacy (LDP) has emerged as a technique of choice for privacy-preserving data collection in several scenarios when the aggregator is not trustworthy. LDP provides client-side privacy by adding noise at the user's
Externí odkaz:
http://arxiv.org/abs/2110.14426
Publikováno v:
In Proceedings of the Combustion Institute 2024 40(1-4)
We present a novel moving immersed boundary method (IBM) and employ it in direct numerical simulations (DNS) of the closed-vessel swirling von Karman flow in laminar and turbulent regimes. The IBM extends direct-forcing approaches by leveraging a tim
Externí odkaz:
http://arxiv.org/abs/2011.04758
Autor:
Wulfmeier, Markus, Byravan, Arunkumar, Hertweck, Tim, Higgins, Irina, Gupta, Ankush, Kulkarni, Tejas, Reynolds, Malcolm, Teplyashin, Denis, Hafner, Roland, Lampe, Thomas, Riedmiller, Martin
Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domains with limited data such as robotics. Can a single generally useful re
Externí odkaz:
http://arxiv.org/abs/2011.01758
Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets. A large body of prior works that investigate GLMs under differential privacy (DP) cons
Externí odkaz:
http://arxiv.org/abs/2011.00467
Autor:
Knapp, Mary, Seager, Sara, Demory, Brice-Olivier, Krishnamurthy, Akshata, Smith, Matthew W., Pong, Christopher M., Bailey, Vanessa P., Donner, Amanda, Di Pasquale, Peter, Campuzano, Brian, Smith, Colin, Luu, Jason, Babuscia, Alessandra, Bocchino, Jr., Robert L., Loveland, Jessica, Colley, Cody, Gedenk, Tobias, Kulkarni, Tejas, Hughes, Kyle, White, Mary, Krajewski, Joel, Fesq, Lorraine
ASTERIA (Arcsecond Space Telescope Enabling Research In Astrophysics) is a 6U CubeSat space telescope (10 cm x 20 cm x 30 cm, 10 kg). ASTERIA's primary mission objective was demonstrating two key technologies for reducing systematic noise in photomet
Externí odkaz:
http://arxiv.org/abs/2005.14155
In this paper, we study the problem of computing $U$-statistics of degree $2$, i.e., quantities that come in the form of averages over pairs of data points, in the local model of differential privacy (LDP). The class of $U$-statistics covers many sta
Externí odkaz:
http://arxiv.org/abs/1910.03861
Autor:
Mellor, John F. J., Park, Eunbyung, Ganin, Yaroslav, Babuschkin, Igor, Kulkarni, Tejas, Rosenbaum, Dan, Ballard, Andy, Weber, Theophane, Vinyals, Oriol, Eslami, S. M. Ali
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with rewards provided by a discriminator network simultaneous
Externí odkaz:
http://arxiv.org/abs/1910.01007
Autor:
Kulkarni, Tejas, Gupta, Ankush, Ionescu, Catalin, Borgeaud, Sebastian, Reynolds, Malcolm, Zisserman, Andrew, Mnih, Volodymyr
The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object r
Externí odkaz:
http://arxiv.org/abs/1906.11883