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2016-01-14
I am now Adjunct Professor at Laval University, Quebec
2023-04-24
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts has been accepted at ICML 2023
2023-01-21
DAG Learning on the Permutahedron has been accepted at ICLR 2023
2022-09-01
On Margins and Generalisation for Voting Classifiers has been accepted at NeurIPS 2022
2022-08-01
moved to Montreal and joined ServiceNow Research
2021-09-28
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound has been accepted at NeurIPS 2021
2021-06-05
excited to mentor FDL-Europe's on-board ML challenge this summer!
2021-01-01
moved to London and joined UCL, INRIA-London
2021-05-08
Learning Binary Trees by Argmin Differentiation was accepted at ICML 2021
2020-08-09
I am speaking at the AI and Space panel at WAI global submit
2019-05-10
excited to join the FDL-Europe this summer!
2019-04-20
I was awarded with the "Prix de Thèse" from the French Society for AI
2019-02-01
I was awarded with the "Prix Excellence Doctorat" from the Université Jean Monnet
2018-12-18
I defended my PhD thesis, entitled "A unified view of local learning: theory and algorithms for enhancing linear models"
2018-12-07
"Communication-Efficient Decentralized Boosting while Discovering the Collaboration Graph" presented at MLPCD 2 NeurIPS Workshop
2018-03-23
Adversarial Robustness Toolbox is now available on github
2017-10-24
2017-09-06
our work on adversarial examples will be presented at AISec2017
2016-08-15
2016-04-06
the paper Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms, co-written with R.Emonet and M.Sebban, is now available on arxiv
2016-03-05
2016-01-14
talk on c2lm at Lives ANR project kick-off at Univ. Marseille
View my profilePublications and preprints
- 2023 Zantedeschi, Long, Piché, Schuster, Drouin
Causal Discovery with Language Models as Imperfect Experts
paper, code
poster at SPIGM @ ICML 2023
- 2023 Etienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
paper, code
accepted at ICML 2023 for publication
- 2022 Zantedeschi, Franceschi, Kaddour, Kusner, Niculae
DAG Learning on the Permutahedron
paper, code
accepted at ICLR 2023 for publication
- 2022 Felix Biggs, Valentina Zantedeschi, Benjamin Guedj
On Margins and Generalisation for Voting Classifiers
paper, code
accepted at NeurIPS 2022 for publication
- 2022 Andrew Wren, Pasquale Minervini, Luca Franceschi, Valentina Zantedeschi
Learning Discrete Directed Acyclic Graphs via Backpropagation
paper
contributed talk at Causality for Real-world Impact NeurIPS 2022 workshop
- 2021 Růžička, Vaughan, De Martini, Fulton, Salvatelli, Bridges, Mateo-Garcia, Zantedeschi
RaVAEn: unsupervised change detection of extreme events using ML on-board satellites
paper, RaVAEn
published in Nature Scientific Reports journal
- 2021 Zantedeschi, Viallard, Morvant, Emonet, Habrard, Germain, Guedj
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
paper, code
accepted at NeurIPS 2021 for publication
- 2020 Valentina Zantedeschi, Matt Kusner, Vlad Niculae
Learning Binary Trees by Argmin Differentiation
paper, code
accepted at ICML 2021 for publication
- 2020 Schroeder de Witt, Tong, Zantedeschi, De Martini, Kalaitzis, Chantry, Watson-Parris, Bilinski
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery
paper, PyRain
accepted at AAAI 2021 for publication
- 2020 Zantedeschi, De Martini, Tong, Schroeder de Witt, Kalaitzis, Bilinski, Chantry, Watson-Parris
Towards Data-Driven Physics-Informed Global Precipitation Forecasting from Satellite Imagery
paper
accepted at AI4 Earth Sciences and CCAI, NeurIPS 2020 Workshops
- 2019 Zantedeschi, Falasca, Douglas, Strange, Kusner, Watson-Parris
CUMULO: A Dataset for Learning Cloud Classes
paper, dataset, code, slides, talk, poster
Best paper award at NeurIPS 2019 Workshop, Tackling Climate Change with Machine Learning
- 2019 Gautheron, Germain, Habrard, Letarte, Morvant, Sebban, Zantedeschi
PAC-Bayes Approaches to Landmark-Based Learning with Random Fourier Features
paper, code
accepted at ECML 2020 for publication
- 2019 Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi
Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph
paper, code
accepted at AISTATS 2020 for publication
- 2019 PhD Dissertation
A unified view of local learning: theory and algorithms for enhancing linear models
manuscript, slides
Best PhD dissertation award from French Society for AI
- 2018 Valentina Zantedeschi, Rémi Emonet, Marc Sebban
Fast and Provably Effective Multi-view Classification with Landmark-based SVM
paper, BibTex
accepted at ECML 2018 for publication
- 2017 Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat
Efficient Defenses against Adversarial Attacks
paper, BibTex, slides, talk
presented at AISEC 2017 and Grehack 2017
- 2017 Valentina Zantedeschi, Rémi Emonet, Marc Sebban
L3SVMs: Landmarks-based Linear Local Support Vectors Machines
paper
- 2016 Valentina Zantedeschi, Rémi Emonet, Marc Sebban
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data
paper
accepted at NIPS 2016 for publication
- 2016 Valentina Zantedeschi, Rémi Emonet, Marc Sebban
Metric Learning as Convex Combination of Local Models with Generalization Guarantees
site
accepted at CVPR 2016 for publication
- 2016 Valentina Zantedeschi, Rémi Emonet, Marc Sebban
Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms
arxiv, BibTex