Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis. - Darfiana Nur; David Allingham; Judith Rousseau; Kerrie L. Mengersen; Ross McVinish (Comput. Stat. Data Anal., 2009)

On the Selection of Initialization and Activation Function for Deep Neural Networks. - Soufiane Hayou; Arnaud Doucet; Judith Rousseau (CoRR, 2018)

On the Impact of the Activation function on Deep Neural Networks Training. - Soufiane Hayou; Arnaud Doucet; Judith Rousseau (ICML, 2019)

Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel. - Soufiane Hayou; Arnaud Doucet; Judith Rousseau (CoRR, 2019)

Sparse Networks with Core-Periphery Structure. - Cian Naik; François Caron; Judith Rousseau (CoRR, 2019)

Stable ResNet. - Soufiane Hayou; Eugenio Clerico; Bobby He; George Deligiannidis; Arnaud Doucet; Judith Rousseau (AISTATS, 2021)

The Curse of Depth in Kernel Regime. - Soufiane Hayou; Arnaud Doucet; Judith Rousseau (ICBINB@NeurIPS, 2021)

Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. - Cian Naik; Judith Rousseau; Trevor Campbell (NeurIPS, 2022)

Bayesian Nonparametrics for Sparse Dynamic Networks. - Cian Naik; François Caron; Judith Rousseau; Yee Whye Teh; Konstantina Palla (ECML/PKDD (5), 2022)

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. - Paul Rosa; Slava Borovitskiy; Alexander Terenin; Judith Rousseau (NeurIPS, 2023)

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. - Paul Rosa; Viacheslav Borovitskiy; Alexander Terenin; Judith Rousseau (CoRR, 2023)

Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions. - Iskander Azangulov; George Deligiannidis; Judith Rousseau (CoRR, 2024)

Adaptive Diffusion Guidance via Stochastic Optimal Control. - Iskander Azangulov; Peter Potaptchik; Qinyu Li; Eddie Aamari; George Deligiannidis; Judith Rousseau (CoRR, 2025)