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08/17/2023
Dual Gauss-Newton Directions for Deep Learning
Inspired by Gauss-Newton-like methods, we study the benefit of leveragin...
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05/18/2023
Modified Gauss-Newton Algorithms under Noise
Gauss-Newton methods and their stochastic version have been widely used ...
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12/10/2022
Stochastic Optimization for Spectral Risk Measures
Spectral risk objectives - also called L-risks - allow for learning syst...
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07/13/2022
Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
We present the implementation of nonlinear control algorithms based on l...
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12/02/2021
Target Propagation via Regularized Inversion
Target Propagation (TP) algorithms compute targets instead of gradients ...
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12/31/2020
Differentiable Programming à la Moreau
The notion of a Moreau envelope is central to the analysis of first-orde...
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02/20/2020
An Elementary Approach to Convergence Guarantees of Optimization Algorithms for Deep Networks
We present an approach to obtain convergence guarantees of optimization ...
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12/30/2019
End-to-end Learning, with or without Labels
We present an approach for end-to-end learning that allows one to jointl...
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03/19/2019
Kernel-based Translations of Convolutional Networks
Convolutional Neural Networks, as most artificial neural networks, are c...
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02/08/2019
