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05/24/2023
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
We aim to deepen the theoretical understanding of Graph Neural Networks ...
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05/23/2023
One-step differentiation of iterative algorithms
In appropriate frameworks, automatic differentiation is transparent to t...
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04/21/2023
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs
We study the convergence of message passing graph neural networks on ran...
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04/17/2023
Provable local learning rule by expert aggregation for a Hawkes network
We propose a simple network of Hawkes processes as a cognitive model cap...
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03/24/2023
Gradient scarcity with Bilevel Optimization for Graph Learning
A common issue in graph learning under the semi-supervised setting is re...
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03/09/2023
On the Robustness of Text Vectorizers
A fundamental issue in natural language processing is the robustness of ...
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02/17/2023
A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
Bilevel optimization problems, which are problems where two optimization...
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07/26/2022
The derivatives of Sinkhorn-Knopp converge
We show that the derivatives of the Sinkhorn-Knopp algorithm, or iterati...
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05/31/2022
Automatic differentiation of nonsmooth iterative algorithms
Differentiation along algorithms, i.e., piggyback propagation of derivat...
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01/31/2022
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
Bilevel optimization, the problem of minimizing a value function which i...
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12/15/2021
Supervised learning of analysis-sparsity priors with automatic differentiation
Sparsity priors are commonly used in denoising and image reconstruction....
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05/27/2021
On the Universality of Graph Neural Networks on Large Random Graphs
We study the approximation power of Graph Neural Networks (GNNs) on late...
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05/04/2021
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Finding the optimal hyperparameters of a model can be cast as a bilevel ...
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10/22/2020
Model identification and local linear convergence of coordinate descent
For composite nonsmooth optimization problems, Forward-Backward algorith...
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06/02/2020
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
We study properties of Graph Convolutional Networks (GCNs) by analyzing ...
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04/20/2020
Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation
Penalized Least Squares are widely used in signal and image processing. ...
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02/20/2020
Implicit differentiation of Lasso-type models for hyperparameter optimization
Setting regularization parameters for Lasso-type estimators is notorious...
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02/07/2020
Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model
In this paper, we analyse classical variants of the Spectral Clustering ...
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11/06/2019
Linear Support Vector Regression with Linear Constraints
This paper studies the addition of linear constraints to the Support Vec...
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07/12/2019
Dual Extrapolation for Sparse Generalized Linear Models
Generalized Linear Models (GLM) form a wide class of regression and clas...
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06/21/2018
Is the 1-norm the best convex sparse regularization?
The 1-norm is a good convex regularization for the recovery of sparse ve...
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03/02/2018
Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations
The 1-norm was proven to be a good convex regularizer for the recovery o...
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07/07/2014
Low Complexity Regularization of Linear Inverse Problems
Inverse problems and regularization theory is a central theme in contemp...
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05/05/2014
Model Consistency of Partly Smooth Regularizers
This paper studies least-square regression penalized with partly smooth ...
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05/07/2012
