Trusting SVM for Piecewise Linear CNNs. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (ICLR (Poster), 2017)

Smooth Loss Functions for Deep Top-k Classification. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (ICLR (Poster), 2018)

Deep Frank-Wolfe For Neural Network Optimization. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (ICLR (Poster), 2019)

Leveraging structure for optimization in deep learning. - Leonard Berrada (2019)

Training Neural Networks for and by Interpolation. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (ICML, 2020)

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 (NeurIPS, 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)

Comment on Stochastic Polyak Step-Size: Performance of ALI-G. - Leonard Berrada; Andrew Zisserman; M. Pawan Kumar (CoRR, 2021)

A Stochastic Bundle Method for Interpolating Networks. - Alasdair Paren; Leonard Berrada; Rudra P. K. Poudel; M. Pawan Kumar (CoRR, 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 Interpolation. - Alasdair Paren; Leonard Berrada; Rudra P. K. Poudel; M. Pawan Kumar (J. Mach. Learn. Res., 2022)

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)

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)

ConvNets Match Vision Transformers at Scale. - Samuel L. Smith; Andrew Brock; Leonard Berrada; Soham De (CoRR, 2023)

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 001; Srivatsan Srinivasan; Guillaume Desjardins; Arnaud Doucet; et al. (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; et al. (CoRR, 2024)

Operationalizing Contextual Integrity in Privacy-Conscious Assistants. - Sahra Ghalebikesabi; Eugene Bagdasaryan; Ren Yi; Itay Yona; Ilia Shumailov; Aneesh Pappu; Chongyang Shi 003; Laura Weidinger; Robert Stanforth; Leonard Berrada; Pushmeet Kohli; Po-Sen Huang; et al. (CoRR, 2024)

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 003; Laura Weidinger; Robert Stanforth; Leonard Berrada; Pushmeet Kohli; Po-Sen Huang; et al. (Trans. Mach. Learn. Res., 2025)