Zobrazeno 1 - 10 of 10 pro vyhledávání: '"Chevallier, Juliette"'
1
Report
Minimax density estimation in the adversarial framework under local differential privacy
Autor: Albert, Mélisande, Chevallier, Juliette, Laurent, Béatrice, Sacko, Ousmane
We consider the problem of nonparametric density estimation under privacy constraints in an adversarial framework. To this end, we study minimax rates under local differential privacy over Sobolev spaces. We first obtain a lower bound which allows us
Externí odkaz: http://arxiv.org/abs/2403.18357
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2
Dissertation/ Thesis
Enabling Autonomy in Commercial Aviation: An Ontology and Framework for Automating Unmanned Aircraft Systems (UAS)
Autor: Chevallier, Juliette
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, June, 2021
Thesis: M.B.A., Massachusetts Institute of Technology, Department of SLOAN, June, 2021
Cataloged from the official PDF of thesis.
Externí odkaz: https://hdl.handle.net/1721.1/138771
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3
Dissertation/ Thesis
Statistical models and stochastic algorithms for the analysis of longitudinal Riemanian manifold valued data with multiple dynamic
Autor: Chevallier, Juliette
Par delà les études transversales, étudier l'évolution temporelle de phénomènes connait un intérêt croissant. En effet, pour comprendre un phénomène, il semble plus adapté de comparer l'évolution des marqueurs de celui-ci au cours du temp
Externí odkaz: http://www.theses.fr/2019SACLX059/document
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4
Akademický článek
A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling
Autor: Allassonnière, Stéphanie, Chevallier, Juliette
Publikováno v: In Computational Statistics and Data Analysis July 2021 159
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5
Akademický článek
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6
A New Class of EM Algorithms. Escaping Local Minima and Handling Intractable Sampling
Autor: Allassonnière, Stéphanie, Chevallier, Juliette
The expectation-maximization (EM) algorithm is a powerful computational technique for maximum likelihood estimation in incomplete data models. When the expectation step cannot be performed in closed form, a stochastic approximation of EM (SAEM) can b
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::86ecf2fe8823549c96468ad9836a4f5d
https://hal.archives-ouvertes.fr/hal-02044722/file/2019_Allassonniere_Chevallier.pdf
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7
A coherent framework for learning spatiotemporal piecewise- geodesic trajectories from longitudinal manifold-valued data
Autor: Chevallier, Juliette, Debavelaere, Vianney, Allassonnière, Stéphanie
This paper provides a coherent framework for studying longitudinal manifold-valued data. We introduce a Bayesian mixed-effects model which allows to estimate both a group-representative piecewise-geodesic trajectory in the Riemannian space of shape a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ecb9fcd746bb789bf2c6e84ca381909e
https://hal.archives-ouvertes.fr/hal-01646298v3/document
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8
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
Autor: Chevallier, Juliette, Oudard, Stéphane, Allassonnière, Stéphanie
Publikováno v: 31st Conference on Neural Information Processing Systems (NIPS 2017)
31st Conference on Neural Information Processing Systems (NIPS 2017), Dec 2017, Long Beach, United States
Neural Information Processing Systems 2017
Neural Information Processing Systems 2017, Dec 2017, Long Beach, CA, United States
Neural Information Processing Systems 2017, Dec 2017, Long Beach, CA, United States.
Neural Information Processing Systems
Neural Information Processing Systems, Dec 2017, Long Beach, United States
International audience; We introduce a hierarchical model which allows to estimate a group-average piecewise-geodesic trajectory in the Riemannian space of measurements and individual variability. This model falls into the well defined mixed-effect m
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::6505ea191bd9266771182e6c8377f05c
https://hal.archives-ouvertes.fr/hal-01646230
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9
A New Class of Stochastic EM Algorithms. Escaping Local Maxima and Handling Intractable Sampling
Autor: Stéphanie Allassonnière, Juliette Chevallier
Publikováno v: Computational Statistics and Data Analysis
Computational Statistics and Data Analysis, Elsevier, 2021, 159, pp.107159. ⟨10.1016/j.csda.2020.107159⟩
Computational Statistics and Data Analysis, 2021, 159, pp.107159. ⟨10.1016/j.csda.2020.107159⟩
International audience; The expectation-maximization (EM) algorithm is a powerful computational technique for maximum likelihood estimation in incomplete data models. When the expectation step cannot be performed in closed form, a stochastic approxim
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59917e194c96d0f30e8713f92842a026
https://hal.archives-ouvertes.fr/hal-02044722v4/document
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10
Akademický článek
High density penetrating electrode arrays for autonomic nerves.
Autor: Burns J, Yee-Hsee Hsieh, Mueller A, Chevallier J, Sriram TS, Lewis SJ, Chew D, Achyuta A, Fiering J
Publikováno v: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2016 Aug; Vol. 2016, pp. 2802-2805.
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