Leonard Berrada

According to our database, Leonard Berrada authored at least 18 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Privacy Awareness for Information-Sharing Assistants: A Case-study on Form-filling with Contextual Integrity.

Sahra Ghalebikesabi, Eugene Bagdasarian, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger, Robert Stanforth, Leonard Berrada, Pushmeet Kohli, Po-Sen Huang, Borja Balle

Trans. Mach. Learn. Res., 2025

2024

Operationalizing Contextual Integrity in Privacy-Conscious Assistants.

Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger, Robert Stanforth, Leonard Berrada, Pushmeet Kohli, Po-Sen Huang, Borja Balle

CoRR, 2024

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models.

Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz Gustavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas

CoRR, 2024

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

Soham De, Samuel L. Smith, Anushan Fernando, Aleksandar Botev, George-Cristian Muraru, Albert Gu, Ruba Haroun, Leonard Berrada, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, Arnaud Doucet, David Budden, Yee Whye Teh, Razvan Pascanu, Nando de Freitas, Caglar Gulcehre

CoRR, 2024

2023

ConvNets Match Vision Transformers at Scale.

Samuel L. Smith, Andrew Brock, Leonard Berrada, Soham De

CoRR, 2023

Unlocking Accuracy and Fairness in Differentially Private Image Classification.

Leonard Berrada, Soham De, Judy Hanwen Shen, Jamie Hayes, Robert Stanforth, David Stutz, Pushmeet Kohli, Samuel L. Smith, Borja Balle

CoRR, 2023

Differentially Private Diffusion Models Generate Useful Synthetic Images.

Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Samuel L. Smith, Olivia Wiles, Borja Balle

CoRR, 2023

2022

A Stochastic Bundle Method for Interpolation.

Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar

J. Mach. Learn. Res., 2022

Unlocking High-Accuracy Differentially Private Image Classification through Scale.

Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, Borja Balle

CoRR, 2022

A Stochastic Bundle Method for Interpolating Networks.

Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar

CoRR, 2022

2021

Comment on Stochastic Polyak Step-Size: Performance of ALI-G.

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

CoRR, 2021

Verifying Probabilistic Specifications with Functional Lagrangians.

Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar

CoRR, 2021

Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications.

Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Training Neural Networks for and by Interpolation.

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Leveraging structure for optimization in deep learning.

Leonard Berrada

PhD thesis, 2019

Deep Frank-Wolfe For Neural Network Optimization.

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Smooth Loss Functions for Deep Top-k Classification.

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Trusting SVM for Piecewise Linear CNNs.

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

Proceedings of the 5th International Conference on Learning Representations, 2017