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L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues
Many recent works in simulation-based inference (SBI) rely on deep generative models to approximate complex, high-dimensional posterior distributions. However, evaluating whether o…
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
Omar Chehab, Alexandre Gramfort, Aapo Hyvarinen
Self-supervised learning is an increasingly popular approach to unsupervised learning, achieving state-of-the-art results. A prevalent approach consists in contrasting data points…
Validation Diagnostics for SBI algorithms based on Normalizing Flows
Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues
Building on the recent trend of new deep generative models known as Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now efficiently accommodate arbitrary co…
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Guillaume Staerman, Cédric Allain, Alexandre Gramfort +1
Temporal point processes (TPP) are a natural tool for modeling event-based data. Among all TPP models, Hawkes processes have proven to be the most widely used, mainly due to their…
Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments
Hicham Janati, Marco Cuturi, Alexandre Gramfort
Several fields in science, from genomics to neuroimaging, require monitoring populations (measures) that evolve with time. These complex datasets, describing dynamics with both tim…
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think
Omar Chehab, Alexandre Gramfort, Aapo Hyvarinen
Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning. Framing the proble…