Valentina Zantedeschi

According to our database, Valentina Zantedeschi authored at least 28 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures.

Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi

CoRR, 2024

2023

Capture the Flag: Uncovering Data Insights with Large Language Models.

Issam H. Laradji, Perouz Taslakian, Sai Rajeswar, Valentina Zantedeschi, Alexandre Lacoste, Nicolas Chapados, David Vázquez, Christopher Pal, Alexandre Drouin

CoRR, 2023

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series.

Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin

CoRR, 2023

Causal Discovery with Language Models as Imperfect Experts.

Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin

CoRR, 2023

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts.

Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados

Proceedings of the International Conference on Machine Learning, 2023

DAG Learning on the Permutahedron.

Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery.

Nicolas Traut, Katja Heuer, Guillaume Lemaître, Anita Beggiato, David Germanaud, Monique Elmaleh, Alban Bethegnies, Laurent Bonnasse-Gahot, Weidong Cai, Stanislas Chambon, Freddy Cliquet, Ayoub Ghriss, Nicolas Guigui, Amicie de Pierrefeu, Meng Wang, Valentina Zantedeschi, Alexandre Boucaud, Joris Van den Bossche, Balázs Kégl, Richard Delorme, Thomas Bourgeron, Roberto Toro, Gaël Varoquaux

NeuroImage, 2022

Learning Discrete Directed Acyclic Graphs via Backpropagation.

Andrew J. Wren, Pasquale Minervini, Luca Franceschi, Valentina Zantedeschi

CoRR, 2022

On Margins and Generalisation for Voting Classifiers.

Felix Biggs, Valentina Zantedeschi, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Unsupervised Change Detection of Extreme Events Using ML On-Board.

Vít Ruzicka, Anna Vaughan, Daniele De Martini, James Fulton, Valentina Salvatelli, Chris Bridges, Gonzalo Mateo-Garcia, Valentina Zantedeschi

CoRR, 2021

Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound.

Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Binary Decision Trees by Argmin Differentiation.

Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae

Proceedings of the 38th International Conference on Machine Learning, 2021

RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery.

Christian Schröder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Alfredo Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

RainBench: Towards Global Precipitation Forecasting from Satellite Imagery.

Christian Schröder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski

CoRR, 2020

Learning Binary Trees via Sparse Relaxation.

Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae

CoRR, 2020

Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting.

Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs.

Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Cumulo: A Dataset for Learning Cloud Classes.

Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris

CoRR, 2019

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting.

Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi

CoRR, 2019

Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph.

Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi

CoRR, 2019

2018

A Unified View of Local Learning : Theory and Algorithms for Enhancing Linear Models. (Une Vue Unifiée de l'Apprentissage Local : Théorie et Algorithmes pour l'Amélioration de Modèles Linéaires).

Valentina Zantedeschi

PhD thesis, 2018

Adversarial Robustness Toolbox v0.2.2.

Maria-Irina Nicolae, Mathieu Sinn, Tran Ngoc Minh, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Ian M. Molloy, Benjamin Edwards

CoRR, 2018

Fast and Provably Effective Multi-view Classification with Landmark-Based SVM.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

2017

L³-SVMs: Landmarks-based Linear Local Support Vectors Machines.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

CoRR, 2017

Efficient Defenses Against Adversarial Attacks.

Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat

Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017

2016

Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

CoRR, 2016

beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Metric Learning as Convex Combinations of Local Models with Generalization Guarantees.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016