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Financial Risk Management with Machine Learning
Related publications (111)
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Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...
EPFL2024Random matrix methods for high-dimensional machine learning models
In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...
EPFL2024Topics in statistical physics of high-dimensional machine learning
In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...
EPFL2024Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks
In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...
EPFL2024Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks
Romain Christophe Rémy Fleury, Janez Rus
The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...
2024Performing and Detecting Backdoor Attacks on Face Recognition Algorithms
The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...
EPFL2024A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
Jan Sickmann Hesthaven, Federico Pichi
The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...
San Diego2024Gibbs sampling the posterior of neural networks
Lenka Zdeborová, Giovanni Piccioli, Emanuele Troiani
In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...
Bristol2024Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model
Yi Zhang, Wenlong Liao, Zhe Yang
Recently, remarkable progress has been made in the application of machine learning (ML) techniques (e.g., neural networks) to transformer fault diagnosis. However, the diagnostic processes employed by these techniques often suffer from a lack of interpreta ...
Piscataway2024ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees
Daniel Gatica-Perez, Sina Sajadmanesh
Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...
Assoc Computing Machinery2024