Zantedeschi, Valentina

27 publications

ICML 2025 Context Is Key: A Benchmark for Forecasting with Essential Textual Information Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin

ICLR 2025 InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation Gaurav Sahu, Abhay Puri, Juan A. Rodriguez, Amirhossein Abaskohi, Mohammad Chegini, Alexandre Drouin, Perouz Taslakian, Valentina Zantedeschi, Alexandre Lacoste, David Vazquez, Nicolas Chapados, Christopher Pal, Sai Rajeswar, Issam H. Laradji

ICLRW 2025 Learning to Defer for Causal Discovery with Imperfect Experts Oscar Clivio, Divyat Mahajan, Perouz Taslakian, Sara Magliacane, Ioannis Mitliagkas, Valentina Zantedeschi, Alexandre Drouin

AISTATS 2025 Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain

NeurIPSW 2024 Context Is Key: A Benchmark for Forecasting with Essential Textual Information Arjun Ashok, Andrew Robert Williams, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin

AISTATS 2024 Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi

ICMLW 2024 Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas, Joao Monteiro, Valentina Zantedeschi

NeurIPS 2024 RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content João Monteiro, Pierre-André Noël, Étienne Marcotte, Sai Rajeswar, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian

NeurIPSW 2024 Sample Compression Unleashed : New Generalization Bounds for Real Valued Losses Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain

NeurIPSW 2024 Sample Compression Unleashed : New Generalization Bounds for Real Valued Losses Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain

ICLR 2024 TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin

NeurIPSW 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 Vazquez, Christopher Pal, Alexandre Drouin

ICMLW 2023 Causal Discovery with Language Models as Imperfect Experts Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin

ICLR 2023 DAG Learning on the Permutahedron Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt Kusner, Vlad Niculae

ICML 2023 Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados

ICLRW 2022 DAG Learning on the Permutahedron Valentina Zantedeschi, Jean Kaddour, Luca Franceschi, Matt Kusner, Vlad Niculae

NeurIPSW 2022 Learning Discrete Directed Acyclic Graphs via Backpropagation Andrew J. Wren, Pasquale Minervini, Luca Franceschi, Valentina Zantedeschi

NeurIPSW 2022 Learning Discrete Directed Acyclic Graphs via Backpropagation Andrew J. Wren, Pasquale Minervini, Luca Franceschi, Valentina Zantedeschi

NeurIPS 2022 On Margins and Generalisation for Voting Classifiers Felix Biggs, Valentina Zantedeschi, Benjamin Guedj

ICML 2021 Learning Binary Decision Trees by Argmin Differentiation Valentina Zantedeschi, Matt Kusner, Vlad Niculae

NeurIPS 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

AAAI 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

AISTATS 2020 Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi

ECML-PKDD 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

ECML-PKDD 2018 Fast and Provably Effective Multi-View Classification with Landmark-Based SVM Valentina Zantedeschi, Rémi Emonet, Marc Sebban

NeurIPS 2016 Beta-Risk: A New Surrogate Risk for Learning from Weakly Labeled Data Valentina Zantedeschi, Rémi Emonet, Marc Sebban

CVPR 2016 Metric Learning as Convex Combinations of Local Models with Generalization Guarantees Valentina Zantedeschi, Remi Emonet, Marc Sebban