35 citations · 143 across the 27 of their papers we have counts for
10 papers · 1 filter
PAVI: Plate-Amortized Variational Inference
Louis Rouillard, Alexandre Le Bris, Thomas Moreau +1
Given observed data and a probabilistic generative model, Bayesian inference searches for the distribution of the model's parameters that could have yielded the data. Inference is…
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
Mathieu Dagréou, Thomas Moreau, Samuel Vaiter +1
Bilevel optimization problems, which are problems where two optimization problems are nested, have more and more applications in machine learning. In many practical cases, the uppe…
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…
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
Mathieu Dagréou, Pierre Ablin, Samuel Vaiter +1
Bilevel optimization, the problem of minimizing a value function which involves the arg-minimum of another function, appears in many areas of machine learning. In a large scale emp…
HNPE: Leveraging Global Parameters for Neural Posterior Estimation
Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe +1
Inferring the parameters of a stochastic model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is stron…
Super-efficiency of automatic differentiation for functions defined as a minimum
Pierre Ablin, Gabriel Peyré, Thomas Moreau
In min-min optimization or max-min optimization, one has to compute the gradient of a function defined as a minimum. In most cases, the minimum has no closed-form, and an approxima…
Learning step sizes for unfolded sparse coding
Pierre Ablin, Thomas Moreau, Mathurin Massias +1
Sparse coding is typically solved by iterative optimization techniques, such as the Iterative Shrinkage-Thresholding Algorithm (ISTA). Unfolding and learning weights of ISTA using…
Understanding the Learned Iterative Soft Thresholding Algorithm with matrix factorization
Thomas Moreau, Joan Bruna
Sparse coding is a core building block in many data analysis and machine learning pipelines. Typically it is solved by relying on generic optimization techniques, such as the Itera…
Post Training in Deep Learning with Last Kernel
Thomas Moreau, Julien Audiffren
One of the main challenges of deep learning methods is the choice of an appropriate training strategy. In particular, additional steps, such as unsupervised pre-training, have been…
Understanding Trainable Sparse Coding via Matrix Factorization
Thomas Moreau, Joan Bruna
Sparse coding is a core building block in many data analysis and machine learning pipelines. Typically it is solved by relying on generic optimization techniques, that are optimal…