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ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective


Jul 21, 2022
Yihong Chen, Pushkar Mishra, Luca Franceschi, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel


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Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions


Jun 03, 2021
Mathias Niepert, Pasquale Minervini, Luca Franceschi


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On the Iteration Complexity of Hypergradient Computation


Jul 10, 2020
Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo

* accepted at ICML 2020; 19 pages, 4 figures; code at https://github.com/prolearner/hypertorch (corrected typos and one reference) 

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Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm


Oct 18, 2019
Michele Donini, Luca Franceschi, Massimiliano Pontil, Orchid Majumder, Paolo Frasconi

* Code available at https://github.com/awslabs/adatune 

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Learning Discrete Structures for Graph Neural Networks


May 17, 2019
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He

* To appear as a conference paper at ICML 2019, code at https://github.com/lucfra/LDS 

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Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs


Feb 15, 2019
Octavio Villarreal, Victor Barasuol, Marco Camurri, Luca Franceschi, Michele Focchi, Massimiliano Pontil, Darwin G. Caldwell, Claudio Semini

* 9 pages, 11 figures. Accepted to RA-L + ICRA 2019, January 2019 

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Bilevel Programming for Hyperparameter Optimization and Meta-Learning


Jul 03, 2018
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimilano Pontil

* ICML 2018; code for replicating experiments at https://github.com/prolearner/hyper-representation, main package (Far-HO) at https://github.com/lucfra/FAR-HO 

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Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning


Jun 13, 2018
Luca Franceschi, Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo, Paolo Frasconi

* This submission is a reduced version of (Franceschi et al., arXiv:1806.04910) which has been accepted at the main ICML 2018 conference. In this paper we illustrate the software framework, material that could not be included in the conference paper 

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