Concept
Théorie de l'apprentissage statistique
Publications associées (34)
Boosting likelihood learning with event reweighting
Andrea Wulzer, Alfredo Glioti, Siyu Chen
Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...
Springer2024Data-Driven Control and Optimization under Noisy and Uncertain Conditions
Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...
EPFL2023Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions
Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...
EPFL2023A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods
Michaël Unser, Pakshal Narendra Bohra
We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape
Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans.The success in practice stems from the ability to successfully train massive neural ...
EPFL2023Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations
Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...
EPFL2023The very knotty lenser: Exploring the role of regularization in source and potential reconstructions using Gaussian process regression
Reconstructing lens potentials and lensed sources can easily become an underconstrained problem, even when the degrees of freedom are low, due to degeneracies, particularly when potential perturbations superimposed on a smooth lens are included. Regulariza ...
OXFORD UNIV PRESS2022Practical Byzantine-resilient Stochastic Gradient Descent
Sébastien Louis Alexandre Rouault
Algorithms are everywhere.The recipe for the frangipane cake is an algorithm.If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven.Now, what ...
EPFL2022Semantic Perturbations with Normalizing Flows for Improved Generalization
Martin Jaggi, Sebastian Urban Stich, Tatjana Chavdarova
Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often limited to a fixed set of hard-coded transformations. Recently, several work ...
IEEE2021Last iterate convergence of SGD for Least-Squares in the Interpolation regime
Nicolas Henri Bernard Flammarion, Aditya Vardhan Varre, Loucas Pillaud-Vivien
Motivated by the recent successes of neural networks that have the ability to fit the data perfectly \emph{and} generalize well, we study the noiseless model in the fundamental least-squares setup. We assume that an optimum predictor fits perfectly inputs ...
2021