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02/01/2023
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds
The privacy preserving properties of Langevin dynamics with additive iso...
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05/28/2021
Quantifying Information Leakage from Gradients
Sharing deep neural networks' gradients instead of training data could f...
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05/25/2021
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs can be Secretly Coded into the Entropy of Classifiers' Outputs
It is known that deep neural networks, trained for the classification of...
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10/17/2020
Layer-wise Characterization of Latent Information Leakage in Federated Learning
Training a deep neural network (DNN) via federated learning allows parti...
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07/15/2020
On stochastic mirror descent with interacting particles: convergence properties and variance reduction
An open problem in optimization with noisy information is the computatio...
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02/14/2020
Optimally weighted loss functions for solving PDEs with Neural Networks
Recent works have shown that deep neural networks can be employed to sol...
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01/31/2020
On Calibration Neural Networks for extracting implied information from American options
Extracting implied information, like volatility and/or dividend, from ob...
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12/18/2019
Analytic expressions for the output evolution of a deep neural network
We present a novel methodology based on a Taylor expansion of the networ...
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04/23/2019
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is propo...
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02/14/2019
Generalisation in fully-connected neural networks for time series forecasting
In this paper we study the generalisation capabilities of fully-connecte...
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10/25/2018
A Gaussian Process perspective on Convolutional Neural Networks
In this paper we cast the well-known convolutional neural network in a G...
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03/14/2017
