Interpretability in deep learning for finance: a case study for the Heston model. - Damiano Brigo; Xiaoshan Huang; Andrea Pallavicini; Haitz Sáez de Ocáriz Borde (CoRR, 2021)

Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow. - Haitz Sáez de Ocáriz Borde; David Sondak; Pavlos Protopapas (CoRR, 2021)

Latent Space based Memory Replay for Continual Learning in Artificial Neural Networks. - Haitz Sáez de Ocáriz Borde (CoRR, 2021)

Sheaf Neural Networks with Connection Laplacians. - Federico Barbero; Cristian Bodnar; Haitz Sáez de Ocáriz Borde; Michael M. Bronstein; Petar Velickovic; Pietro Liò (TAG-ML, 2022)

Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression. - Haitz Sáez de Ocáriz Borde; Federico Barbero (CoRR, 2022)

Latent Graph Inference using Product Manifolds. - Haitz Sáez de Ocáriz Borde; Anees Kazi; Federico Barbero; Pietro Liò (ICLR, 2023)

AMES: A differentiable embedding space selection framework for latent graph inference. - Yuan Lu; Haitz Sáez de Ocáriz Borde; Pietro Liò (NeurReps, 2023)

Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization. - Haitz Sáez de Ocáriz Borde; Alvaro Arroyo; Ismael Morales; Ingmar Posner; Xiaowen Dong 001 (NeurIPS, 2023)

Projections of Model Spaces for Latent Graph Inference. - Haitz Sáez de Ocáriz Borde; Alvaro Arroyo; Ingmar Posner (CoRR, 2023)

Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces. - Haitz Sáez de Ocáriz Borde; Alvaro Arroyo; Ismael Morales; Ingmar Posner; Xiaowen Dong 001 (CoRR, 2023)

Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries. - Haitz Sáez de Ocáriz Borde; Anastasis Kratsios (ICLR, 2024)

Asymmetry in Low-Rank Adapters of Foundation Models. - Jiacheng Zhu; Kristjan H. Greenewald; Kimia Nadjahi; Haitz Sáez de Ocáriz Borde; Rickard Brüel Gabrielsson; Leshem Choshen; Marzyeh Ghassemi; Mikhail Yurochkin; Justin Solomon 001 (ICML, 2024)

Score Distillation via Reparametrized DDIM. - Artem Lukoianov; Haitz Sáez de Ocáriz Borde; Kristjan H. Greenewald; Vitor Guizilini; Timur M. Bagautdinov; Vincent Sitzmann; Justin M. Solomon (NeurIPS, 2024)

Breaking the Curse of Dimensionality with Distributed Neural Computation. - Haitz Sáez de Ocáriz Borde; Takashi Furuya; Anastasis Kratsios; Marc T. Law (CoRR, 2024)

Metric Learning for Clifford Group Equivariant Neural Networks. - Riccardo Ali; Paulina Kulyte; Haitz Sáez de Ocáriz Borde; Pietro Liò (CoRR, 2024)

Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning. - Haitz Sáez de Ocáriz Borde; Artem Lukoianov; Anastasis Kratsios; Michael M. Bronstein; Xiaowen Dong 001 (CoRR, 2024)

Capacity bounds for hyperbolic neural network representations of latent tree structures. - Anastasis Kratsios; Ruiyang Hong; Haitz Sáez de Ocáriz Borde (Neural Networks, 2024)

DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer. - Yizhe Wu; Haitz Sáez de Ocáriz Borde; Jack Collins; Oiwi Parker Jones; Ingmar Posner (IEEE Robotics Autom. Lett., 2024)

Neural Spacetimes for DAG Representation Learning. - Haitz Sáez de Ocáriz Borde; Anastasis Kratsios; Marc T. Law; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2025)

Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts. - Anastasis Kratsios; Haitz Sáez de Ocáriz Borde; Takashi Furuya; Marc T. Law (Trans. Mach. Learn. Res., 2025)

Closed-Form Diffusion Models. - Christopher Scarvelis; Haitz Sáez de Ocáriz Borde; Justin Solomon 001 (Trans. Mach. Learn. Res., 2025)

Keep It Light! Simplifying Image Clustering Via Text-Free Adapters. - Yicen Li; Haitz Sáez de Ocáriz Borde; Anastasis Kratsios; Paul D. McNicholas (CoRR, 2025)

Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement. - Huidong Liang; Haitz Sáez de Ocáriz Borde; Baskaran Sripathmanathan; Michael M. Bronstein; Xiaowen Dong 001 (CoRR, 2025)

Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization. - Matteo Gallici; Haitz Sáez de Ocáriz Borde (CoRR, 2025)

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters. - Anastasis Kratsios; Tin Sum Cheng; Aurélien Lucchi; Haitz Sáez de Ocáriz Borde (CoRR, 2025)

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs. - Reza Arabpour; Haitz Sáez de Ocáriz Borde; Anastasis Kratsios (CoRR, 2025)

Beyond Parallelism: Synergistic Computational Graph Effects in Multi-Head Attention. - Haitz Sáez de Ocáriz Borde (CoRR, 2025)

Structured Captions Improve Prompt Adherence in Text-to-Image Models (Re-LAION-Caption 19M). - Nicholas Merchant; Haitz Sáez de Ocáriz Borde; Andrei Cristian Popescu; Carlos Garcia Jurado Suarez (CoRR, 2025)

Mathematical Foundations of Geometric Deep Learning. - Haitz Sáez de Ocáriz Borde; Michael M. Bronstein (CoRR, 2025)