Juliette Chevallier
- Institut de Mathématiques de Toulouse UMR5219 (IMT)
- Institut National des Sciences Appliquées - Toulouse (INSA Toulouse)
Présentation
Publications
Publications
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Minimax density estimation in the adversarial framework under local differential privacy2025 |
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A New Class of Stochastic EM Algorithms. Escaping Local Maxima and Handling Intractable SamplingComputational Statistics and Data Analysis, 2021, 159, pp.107159. ⟨10.1016/j.csda.2020.107159⟩ |
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A coherent framework for learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued dataSIAM Journal on Imaging Sciences, 2021, 14 (1), pp.349-388. ⟨10.1137/20M1328026⟩ |
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A Scaled Poisson Bayesian Model for Viral Epidemic MonitoringICASSP, May 2026, Barcelone, Spain |
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Hierarchical Bayesian Estimation of COVID-19 Reproduction Number2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr 2025, Hyderabad (IN), India |
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Étude de modèles de diffusion pour la modification d'images de danse sportive56èmes Journées de Statistiques de la Société Française de Statistique, Société Française de Statistique, Jun 2025, Aix (Aix-Marseille Université), France |
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Sampling Nonsmooth Log-Concave Densities: A Comparative Study of Primal-Dual Based Proposal Distributions2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr 2025, Hyderabad (IN), India |
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Pandemic Intensity Estimation from Stochastic Approximation-based Algorithms2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Dec 2023, Herradura, Costa Rica. ⟨10.1109/CAMSAP58249.2023.10403431⟩ |
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Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data31st Conference on Neural Information Processing Systems (NIPS 2017), Dec 2017, Long Beach, United States |
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Statistical models and stochastic algorithms for the analysis of longitudinal Riemanian manifold valued data with multiple dynamicStatistics [math.ST]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLX059⟩ |
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Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued dataNeural Information Processing Systems 2017, Dec 2017, Long Beach, CA, United States. |