Sampling and Reconstruction of Signals on Product Graphs. - Guillermo Ortiz-Jiménez; Mario Coutino; Sundeep Prabhakar Chepuri; Geert Leus (GlobalSIP, 2018)

CDOT: Continuous Domain Adaptation using Optimal Transport. - Guillermo Ortiz-Jiménez; Mireille El Gheche; Effrosyni Simou; Hermina Petric Maretic; Pascal Frossard (CoRR, 2019)

Sparse Sampling for Inverse Problems With Tensors. - Guillermo Ortiz-Jiménez; Mario Coutino; Sundeep Prabhakar Chepuri; Geert Leus (IEEE Trans. Signal Process., 2019)

Forward-Backward Splitting for Optimal Transport Based Problems. - Guillermo Ortiz-Jiménez; Mireille El Gheche; Effrosyni Simou; Hermina Petric Maretic; Pascal Frossard (ICASSP, 2020)

On The Choice of Graph Neural Network Architectures. - Clément Vignac; Guillermo Ortiz-Jiménez; Pascal Frossard (ICASSP, 2020)

Hold me tight! Influence of discriminative features on deep network boundaries. - Guillermo Ortiz-Jiménez; Apostolos Modas; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard (NeurIPS, 2020)

Neural Anisotropy Directions. - Guillermo Ortiz-Jiménez; Apostolos Modas; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard (NeurIPS, 2020)

What can linearized neural networks actually say about generalization? - Guillermo Ortiz-Jiménez; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard (NeurIPS, 2021)

A neural anisotropic view of underspecification in deep learning. - Guillermo Ortiz-Jiménez; Itamar Franco Salazar-Reque; Apostolos Modas; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard (CoRR, 2021)

Optimism in the Face of Adversity: Understanding and Improving Deep Learning Through Adversarial Robustness. - Guillermo Ortiz-Jiménez; Apostolos Modas; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard (Proc. IEEE, 2021)

A Structured Dictionary Perspective on Implicit Neural Representations. - Gizem Yüce; Guillermo Ortiz-Jiménez; Beril Besbinar; Pascal Frossard (CVPR, 2022)

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions. - Apostolos Modas; Rahul Rade; Guillermo Ortiz-Jiménez; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard (ECCV (25), 2022)

On the benefits of knowledge distillation for adversarial robustness. - Javier Maroto; Guillermo Ortiz-Jiménez; Pascal Frossard (CoRR, 2022)

Catastrophic overfitting is a bug but also a feature. - Guillermo Ortiz-Jiménez; Pau de Jorge; Amartya Sanyal; Adel Bibi; Puneet K. Dokania; Pascal Frossard; Grégory Rogez; Philip H. S. Torr (CoRR, 2022)

When does Privileged information Explain Away Label Noise? - Guillermo Ortiz-Jiménez; Mark Collier; Anant Nawalgaria; Alexander Nicholas D'Amour; Jesse Berent; Rodolphe Jenatton; Efi Kokiopoulou (ICML, 2023)

Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models. - Guillermo Ortiz-Jiménez; Alessandro Favero; Pascal Frossard (NeurIPS, 2023)

Catastrophic overfitting can be induced with discriminative non-robust features. - Guillermo Ortiz-Jiménez; Pau de Jorge; Amartya Sanyal; Adel Bibi; Puneet K. Dokania; Pascal Frossard; Grégory Rogez; Philip Torr 001 (Trans. Mach. Learn. Res., 2023)

Localizing Task Information for Improved Model Merging and Compression. - Ke Wang; Nikolaos Dimitriadis; Guillermo Ortiz-Jiménez; François Fleuret; Pascal Frossard (ICML, 2024)

Pi-DUAL: Using privileged information to distinguish clean from noisy labels. - Ke Wang; Guillermo Ortiz-Jiménez; Rodolphe Jenatton; Mark Collier; Efi Kokiopoulou; Pascal Frossard (ICML, 2024)

UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI. - Ilia Shumailov; Jamie Hayes; Eleni Triantafillou; Guillermo Ortiz-Jiménez; Nicolas Papernot; Matthew Jagielski; Itay Yona; Heidi Howard; Eugene Bagdasaryan (CoRR, 2024)

On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark. - Jaiden Fairoze; Guillermo Ortiz-Jiménez; Mel Vecerík; Somesh Jha; Sven Gowal (AISTATS, 2025)

LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging. - Ke Wang; Nikolaos Dimitriadis; Alessandro Favero; Guillermo Ortiz-Jiménez; François Fleuret; Pascal Frossard (ICLR, 2025)