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ORCIDJudith Rousseau
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- affiliation: Université Paris Dauphine, France
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2020 – today
- 2026
[i11]Joshua J. Bon, James Bailie, Judith Rousseau, Christian P. Robert:
Persuasive Privacy. CoRR abs/2601.22945 (2026)- [i10]Sandra Fortini, Kenyon Ng, Sonia Petrone, Judith Rousseau, Susan Wei:
A principled framework for uncertainty decomposition in TabPFN. CoRR abs/2602.04596 (2026) - 2025
- [j3]Paul Rosa, Judith Rousseau:
Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds. J. Mach. Learn. Res. 26: 164:1-164:65 (2025) - [j2]Déborah Sulem, Vincent Rivoirard, Judith Rousseau:
Scalable and Adaptive Variational Bayes Methods for Hawkes Processes. J. Mach. Learn. Res. 26: 217:1-217:102 (2025) - [i9]Iskander Azangulov, Peter Potaptchik, Qinyu Li, Eddie Aamari, George Deligiannidis, Judith Rousseau:
Adaptive Diffusion Guidance via Stochastic Optimal Control. CoRR abs/2505.19367 (2025) - 2024
- [i8]Iskander Azangulov, George Deligiannidis, Judith Rousseau:
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions. CoRR abs/2409.18804 (2024) - 2023
- [c6]Paul Rosa, Slava Borovitskiy, Alexander Terenin, Judith Rousseau:
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. NeurIPS 2023 - [i7]Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin, Judith Rousseau:
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. CoRR abs/2309.10918 (2023) - 2022
- [c5]Cian Naik, Judith Rousseau, Trevor Campbell:
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. NeurIPS 2022 - [c4]Cian Naik
, François Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla:
Bayesian Nonparametrics for Sparse Dynamic Networks. ECML/PKDD (5) 2022: 191-206 - [i6]Cian Naik, Judith Rousseau, Trevor Campbell:
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. CoRR abs/2203.09675 (2022) - 2021
- [c3]Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau:
Stable ResNet. AISTATS 2021: 1324-1332 - [c2]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
The Curse of Depth in Kernel Regime. ICBINB@NeurIPS 2021: 41-47 - 2020
- [i5]Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau:
Stable ResNet. CoRR abs/2010.12859 (2020)
2010 – 2019
- 2019
- [c1]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Impact of the Activation function on Deep Neural Networks Training. ICML 2019: 2672-2680 - [i4]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Impact of the Activation Function on Deep Neural Networks Training. CoRR abs/1902.06853 (2019) - [i3]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel. CoRR abs/1905.13654 (2019) - [i2]Cian Naik, François Caron, Judith Rousseau:
Sparse Networks with Core-Periphery Structure. CoRR abs/1910.09679 (2019) - 2018
- [i1]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Selection of Initialization and Activation Function for Deep Neural Networks. CoRR abs/1805.08266 (2018)
2000 – 2009
- 2009
- [j1]Darfiana Nur, David Allingham, Judith Rousseau, Kerrie L. Mengersen, Ross McVinish:
Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis. Comput. Stat. Data Anal. 53(5): 1873-1882 (2009)
Coauthor Index
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