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2020 – today
- 2024
[c15]Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi:
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. AISTATS 2024: 3007-3015- [c14]João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian:
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference. EMNLP (Findings) 2024: 15284-15302 - [c13]Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin:
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series. ICLR 2024 - [i23]Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi:
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. CoRR abs/2402.13285 (2024) - [i22]João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian:
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference. CoRR abs/2404.15420 (2024) - [i21]João Monteiro, Pierre-André Noël, Étienne Marcotte, Sai Rajeswar, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian:
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content. CoRR abs/2406.11811 (2024) - [i20]Gaurav Sahu, Abhay Puri, Juan A. Rodriguez, Alexandre Drouin, Perouz Taslakian, Valentina Zantedeschi, Alexandre Lacoste, David Vázquez, Nicolas Chapados, Christopher Pal, Sai Rajeswar, Issam Hadj Laradji:
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation. CoRR abs/2407.06423 (2024) - [i19]Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin:
Context is Key: A Benchmark for Forecasting with Essential Textual Information. CoRR abs/2410.18959 (2024) - 2023
- [c12]Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae:
DAG Learning on the Permutahedron. ICLR 2023 - [c11]Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados:
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. ICML 2023: 23958-24004 - [i18]Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae:
DAG Learning on the Permutahedron. CoRR abs/2301.11898 (2023) - [i17]Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados:
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. CoRR abs/2304.09836 (2023) - [i16]Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin:
Causal Discovery with Language Models as Imperfect Experts. CoRR abs/2307.02390 (2023) - [i15]Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin:
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series. CoRR abs/2310.01327 (2023) - [i14]Issam H. Laradji, Perouz Taslakian, Sai Rajeswar, Valentina Zantedeschi, Alexandre Lacoste, Nicolas Chapados, David Vázquez, Christopher Pal, Alexandre Drouin:
Capture the Flag: Uncovering Data Insights with Large Language Models. CoRR abs/2312.13876 (2023) - 2022
- [j1]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:
Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery. NeuroImage 255: 119171 (2022) - [c10]Felix Biggs, Valentina Zantedeschi, Benjamin Guedj:
On Margins and Generalisation for Voting Classifiers. NeurIPS 2022 - [i13]Felix Biggs, Valentina Zantedeschi, Benjamin Guedj:
On Margins and Generalisation for Voting Classifiers. CoRR abs/2206.04607 (2022) - [i12]Andrew J. Wren, Pasquale Minervini, Luca Franceschi, Valentina Zantedeschi:
Learning Discrete Directed Acyclic Graphs via Backpropagation. CoRR abs/2210.15353 (2022) - 2021
- [c9]Christian Schröder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Alfredo Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski:
RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery. AAAI 2021: 14902-14910 - [c8]Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae:
Learning Binary Decision Trees by Argmin Differentiation. ICML 2021: 12298-12309 - [c7]Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. NeurIPS 2021: 455-467 - [i11]Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. CoRR abs/2106.12535 (2021) - [i10]Vít Ruzicka, Anna Vaughan, Daniele De Martini, James Fulton, Valentina Salvatelli, Chris Bridges, Gonzalo Mateo-Garcia, Valentina Zantedeschi:
Unsupervised Change Detection of Extreme Events Using ML On-Board. CoRR abs/2111.02995 (2021) - 2020
- [c6]Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi:
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. AISTATS 2020: 864-874 - [c5]Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi:
Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting. ECML/PKDD (3) 2020: 141-157 - [i9]Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae:
Learning Binary Trees via Sparse Relaxation. CoRR abs/2010.04627 (2020) - [i8]Christian Schröder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski:
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery. CoRR abs/2012.09670 (2020)
2010 – 2019
- 2019
- [i7]Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi:
Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph. CoRR abs/1901.08460 (2019) - [i6]Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi:
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. CoRR abs/1906.06203 (2019) - [i5]Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris:
Cumulo: A Dataset for Learning Cloud Classes. CoRR abs/1911.04227 (2019) - 2018
- [b1]Valentina Zantedeschi:
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). University of Lyon, France, 2018 - [c4]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
Fast and Provably Effective Multi-view Classification with Landmark-Based SVM. ECML/PKDD (2) 2018: 193-208 - [i4]Maria-Irina Nicolae, Mathieu Sinn, Tran Ngoc Minh, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Ian M. Molloy, Benjamin Edwards:
Adversarial Robustness Toolbox v0.2.2. CoRR abs/1807.01069 (2018) - 2017
- [c3]Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat:
Efficient Defenses Against Adversarial Attacks. AISec@CCS 2017: 39-49 - [i3]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
L3-SVMs: Landmarks-based Linear Local Support Vectors Machines. CoRR abs/1703.00284 (2017) - [i2]Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat:
Efficient Defenses Against Adversarial Attacks. CoRR abs/1707.06728 (2017) - 2016
- [c2]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
Metric Learning as Convex Combinations of Local Models with Generalization Guarantees. CVPR 2016: 1478-1486 - [c1]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data. NIPS 2016: 4358-4366 - [i1]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms. CoRR abs/1604.01376 (2016)
Coauthor Index
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