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pro vyhledávání: '"A, Lécuyer"'
Akademický článek
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Akademický článek
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Autor:
Van der Lee, Gaël, Lécuyer, Anatole, Naud, Maxence, Scherer, Reinhold, Cabestaing, François, Si-Mohammed, Hakim
Vection, the visual illusion of self-motion, provides a strong marker of the VR user experience and plays an important role in both presence and cybersickness. Traditional measurements have been conducted using questionnaires, which exhibit inherent
Externí odkaz:
http://arxiv.org/abs/2412.18445
Deep learning time-series models are often used to make forecasts that inform downstream decisions. Since these decisions can differ from those in the training set, there is an implicit requirement that time-series models will generalize outside of t
Externí odkaz:
http://arxiv.org/abs/2411.00126
We propose Adaptive Randomized Smoothing (ARS) to certify the predictions of our test-time adaptive models against adversarial examples. ARS extends the analysis of randomized smoothing using $f$-Differential Privacy to certify the adaptive compositi
Externí odkaz:
http://arxiv.org/abs/2406.10427
Autor:
Tholoniat, Pierre, Kostopoulou, Kelly, McNeely, Peter, Sodhi, Prabhpreet Singh, Varanasi, Anirudh, Case, Benjamin, Cidon, Asaf, Geambasu, Roxana, Lécuyer, Mathias
Publikováno v:
In ACM SIGOPS 30th Symposium on Operating Systems Principles (SOSP '24), November 4-6, 2024, Austin, TX, USA. ACM, New York, NY, USA, 27 pages
With the impending removal of third-party cookies from major browsers and the introduction of new privacy-preserving advertising APIs, the research community has a timely opportunity to assist industry in qualitatively improving the Web's privacy. Th
Externí odkaz:
http://arxiv.org/abs/2405.16719
We study Thompson Sampling-based algorithms for stochastic bandits with bounded rewards. As the existing problem-dependent regret bound for Thompson Sampling with Gaussian priors [Agrawal and Goyal, 2017] is vacuous when $T \le 288 e^{64}$, we derive
Externí odkaz:
http://arxiv.org/abs/2405.01010
Autor:
Robardey-Eppstein, Sylviane
Publikováno v:
Revue d'Histoire littéraire de la France, 2019 Jul 01. 119(3), 716-718.
Externí odkaz:
https://www.jstor.org/stable/26853437
Autor:
Kazmi, Mishaal, Lautraite, Hadrien, Akbari, Alireza, Tang, Qiaoyue, Soroco, Mauricio, Wang, Tao, Gambs, Sébastien, Lécuyer, Mathias
We present PANORAMIA, a privacy leakage measurement framework for machine learning models that relies on membership inference attacks using generated data as non-members. By relying on generated non-member data, PANORAMIA eliminates the common depend
Externí odkaz:
http://arxiv.org/abs/2402.09477
The Adam optimizer is a popular choice in contemporary deep learning, due to its strong empirical performance. However we observe that in privacy sensitive scenarios, the traditional use of Differential Privacy (DP) with the Adam optimizer leads to s
Externí odkaz:
http://arxiv.org/abs/2312.14334