Kernel Computations from Large-Scale Random Features Obtained by Optical Processing Units. - Ruben Ohana; Jonas Wacker; Jonathan Dong; Sébastien Marmin; Florent Krzakala; Maurizio Filippone; Laurent Daudet (ICASSP, 2020)

Reservoir Computing meets Recurrent Kernels and Structured Transforms. - Jonathan Dong; Ruben Ohana; Mushegh Rafayelyan; Florent Krzakala (NeurIPS, 2020)

The dynamics of learning with feedback alignment. - Maria Refinetti; Stéphane d'Ascoli; Ruben Ohana; Sebastian Goldt (CoRR, 2020)

LightOn Optical Processing Unit : Scaling-up AI and HPC with a Non von Neumann co-processor. - Charles Brossollet; Alessandro Cappelli; Igor Carron; Charidimos Chaintoutis; Amélie Chatelain; Laurent Daudet; Sylvain Gigan; Daniel Hesslow; Florent Krzakala; Julien Launay; Safa Mokaadi; Fabien Moreau; et al. (HCS, 2021)

Photonic co-processors in HPC: Using LightOn OPUs for Randomized Numerical Linear Algebra. - Daniel Hesslow; Alessandro Cappelli; Igor Carron; Laurent Daudet; Raphaël Lafargue; Kilian Müller; Ruben Ohana; Gustave Pariente; Iacopo Poli (HCS, 2021)

Align, then memorise: the dynamics of learning with feedback alignment. - Maria Refinetti; Stéphane d'Ascoli; Ruben Ohana; Sebastian Goldt (ICML, 2021)

Photonic Differential Privacy with Direct Feedback Alignment. - Ruben Ohana; Hamlet Jesse Medina Ruiz; Julien Launay; Alessandro Cappelli; Iacopo Poli; Liva Ralaivola; Alain Rakotomamonjy (NeurIPS, 2021)

ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients. - Alessandro Cappelli; Julien Launay; Laurent Meunier; Ruben Ohana; Iacopo Poli (CoRR, 2021)

Adversarial Robustness by Design Through Analog Computing And Synthetic Gradients. - Alessandro Cappelli; Ruben Ohana; Julien Launay; Laurent Meunier; Iacopo Poli; Florent Krzakala (ICASSP, 2022)

Complex-to-Real Random Features for Polynomial Kernels. - Jonas Wacker; Ruben Ohana; Maurizio Filippone (CoRR, 2022)

Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel. - Jonas Wacker; Ruben Ohana; Maurizio Filippone (AISTATS, 2023)

Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. - Ruben Ohana; Kimia Nadjahi; Alain Rakotomamonjy; Liva Ralaivola (ICML, 2023)

xVal: A Continuous Number Encoding for Large Language Models. - Siavash Golkar; Mariel Pettee; Michael Eickenberg; Alberto Bietti; Miles D. Cranmer; Géraud Krawezik; François Lanusse; Michael McCabe; Ruben Ohana; Liam Holden Parker; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

Multiple Physics Pretraining for Physical Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (CoRR, 2023)

AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. - François Lanusse; Liam Holden Parker; Siavash Golkar; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Géraud Krawezik; Michael McCabe; Ruben Ohana; Mariel Pettee; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

Removing Dust from CMB Observations with Diffusion Models. - David Heurtel-Depeiges; Blakesley Burkhart; Ruben Ohana; Bruno Régaldo-Saint Blancard (CoRR, 2023)

Listening to the noise: Blind Denoising with Gibbs Diffusion. - David Heurtel-Depeiges; Charles Margossian; Ruben Ohana; Bruno Régaldo-Saint Blancard (ICML, 2024)

MoMo: Momentum Models for Adaptive Learning Rates. - Fabian Schaipp; Ruben Ohana; Michael Eickenberg; Aaron Defazio; Robert M. Gower (ICML, 2024)

Multiple Physics Pretraining for Spatiotemporal Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (NeurIPS, 2024)

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. - Ruben Ohana; Michael McCabe; Lucas Meyer; Rudy Morel; Fruzsina J. Agocs; Miguel Beneitez; Marsha Berger; Blakesley Burkhart; Stuart B. Dalziel; Drummond B. Fielding; Daniel Fortunato; Jared A. Goldberg; et al. (NeurIPS, 2024)

Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. - Siavash Golkar; Alberto Bietti; Mariel Pettee; Michael Eickenberg; Miles D. Cranmer; Keiya Hirashima; Géraud Krawezik; Nicholas Lourie; Michael McCabe; Rudy Morel; Ruben Ohana; Liam Holden Parker; et al. (CoRR, 2024)

Optical training of large-scale Transformers and deep neural networks with direct feedback alignment. - Ziao Wang; Kilian Müller; Matthew J. Filipovich; Julien Launay; Ruben Ohana; Gustave Pariente; Safa Mokaadi; Charles Brossollet; Fabien Moreau; Alessandro Cappelli; Iacopo Poli; Igor Carron; et al. (CoRR, 2024)

Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation. - François Rozet; Ruben Ohana; Michael McCabe; Gilles Louppe; François Lanusse; Shirley Ho (CoRR, 2025)

Controllable Patching for Compute-Adaptive Surrogate Modeling of Partial Differential Equations. - Payel Mukhopadhyay; Michael McCabe; Ruben Ohana; Miles D. Cranmer (CoRR, 2025)