A Generalised Signature Method for Time Series. - James Morrill; Adeline Fermanian; Patrick Kidger; Terry J. Lyons (CoRR, 2020)

Framing RNN as a kernel method: A neural ODE approach. - Adeline Fermanian; Pierre Marion; Jean-Philippe Vert; Gérard Biau (NeurIPS, 2021)

Embedding and learning with signatures. - Adeline Fermanian (Comput. Stat. Data Anal., 2021)

Scaling ResNets in the Large-depth Regime. - Pierre Marion; Adeline Fermanian; Gérard Biau; Jean-Philippe Vert (CoRR, 2022)

Functional linear regression with truncated signatures. - Adeline Fermanian (J. Multivar. Anal., 2022)

Learning the Dynamics of Sparsely Observed Interacting Systems. - Linus Bleistein; Adeline Fermanian; Anne-Sophie Jannot; Agathe Guilloux (ICML, 2023)

New directions in the applications of rough path theory. - Adeline Fermanian; Terry J. Lyons; James Morrill; Cristopher Salvi (CoRR, 2023)

Dynamic Survival Analysis with Controlled Latent States. - Linus Bleistein; Van-Tuan Nguyen; Adeline Fermanian; Agathe Guilloux (ICML, 2024)

Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation. - Sobihan Surendran; Adeline Fermanian; Antoine Godichon-Baggioni; Sylvain Le Corff (NeurIPS, 2024)

Dynamical Survival Analysis with Controlled Latent States. - Linus Bleistein; Van-Tuan Nguyen; Adeline Fermanian; Agathe Guilloux (CoRR, 2024)

Multivariate Online Linear Regression for Hierarchical Forecasting. - Massil Hihat; Guillaume Garrigos; Adeline Fermanian; Simon Bussy (CoRR, 2024)

Latent Guided Sampling for Combinatorial Optimization. - Sobihan Surendran; Adeline Fermanian; Sylvain Le Corff (CoRR, 2025)