Metric Learning as Convex Combinations of Local Models with Generalization Guarantees. - Valentina Zantedeschi; Rémi Emonet; Marc Sebban (CVPR, 2016)

beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data. - Valentina Zantedeschi; Rémi Emonet; Marc Sebban (NIPS, 2016)

Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms. - Valentina Zantedeschi; Rémi Emonet; Marc Sebban (CoRR, 2016)

Efficient Defenses Against Adversarial Attacks. - Valentina Zantedeschi; Maria-Irina Nicolae; Ambrish Rawat (AISec@CCS, 2017)

L3-SVMs: Landmarks-based Linear Local Support Vectors Machines. - Valentina Zantedeschi; Rémi Emonet; Marc Sebban (CoRR, 2017)

Fast and Provably Effective Multi-view Classification with Landmark-Based SVM. - Valentina Zantedeschi; Rémi Emonet; Marc Sebban (ECML/PKDD (2), 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)

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 (2018)

Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph. - Valentina Zantedeschi; Aurélien Bellet; Marc Tommasi (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)

Cumulo: A Dataset for Learning Cloud Classes. - Valentina Zantedeschi; Fabrizio Falasca; Alyson Douglas; Richard Strange; Matt J. Kusner; Duncan Watson-Parris (CoRR, 2019)

Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. - Valentina Zantedeschi; Aurélien Bellet; Marc Tommasi (AISTATS, 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 (3), 2020)

Learning Binary Trees via Sparse Relaxation. - Valentina Zantedeschi; Matt J. Kusner; Vlad Niculae (CoRR, 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)

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 (AAAI, 2021)

Learning Binary Decision Trees by Argmin Differentiation. - Valentina Zantedeschi; Matt J. Kusner; Vlad Niculae (ICML, 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 (NeurIPS, 2021)

Unsupervised Change Detection of Extreme Events Using ML On-Board. - Vít Ruzicka; Anna Vaughan; Daniele De Martini; James Fulton; Valentina Salvatelli; Chris Bridges 001; Gonzalo Mateo-Garcia; Valentina Zantedeschi (CoRR, 2021)

On Margins and Generalisation for Voting Classifiers. - Felix Biggs; Valentina Zantedeschi; Benjamin Guedj (NeurIPS, 2022)

Learning Discrete Directed Acyclic Graphs via Backpropagation. - Andrew J. Wren; Pasquale Minervini; Luca Franceschi 001; Valentina Zantedeschi (CoRR, 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 002; Stanislas Chambon; Freddy Cliquet; Ayoub Ghriss; et al. (NeuroImage, 2022)

DAG Learning on the Permutahedron. - Valentina Zantedeschi; Luca Franceschi 001; Jean Kaddour; Matt J. Kusner; Vlad Niculae (ICLR, 2023)

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

Causal Discovery with Language Models as Imperfect Experts. - Stephanie Long; Alexandre Piché; Valentina Zantedeschi; Tibor Schuster; Alexandre Drouin (CoRR, 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 001; Christopher Pal; Alexandre Drouin (CoRR, 2023)

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

XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference. - João Monteiro 002; Étienne Marcotte; Pierre-André Noël; Valentina Zantedeschi; David Vázquez 001; Nicolas Chapados; Christopher Pal; Perouz Taslakian (EMNLP (Findings), 2024)

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

RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content. - João Monteiro 002; Pierre-André Noël; Étienne Marcotte; Sai Rajeswar Mudumba; Valentina Zantedeschi; David Vázquez 001; Nicolas Chapados; Chris Pal; Perouz Taslakian (NeurIPS, 2024)

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation. - Gaurav Sahu; Abhay Puri; Juan A. Rodríguez; Alexandre Drouin; Perouz Taslakian; Valentina Zantedeschi; Alexandre Lacoste; David Vázquez 001; Nicolas Chapados; Christopher Pal; Sai Rajeswar; Issam Hadj Laradji (CoRR, 2024)

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 (CoRR, 2024)

Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones. - Mehrnaz Mofakhami; Reza Bayat; Ioannis Mitliagkas; João Monteiro 002; Valentina Zantedeschi (CoRR, 2024)

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

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation. - Gaurav Sahu; Abhay Puri; Juan A. Rodríguez; Amirhossein Abaskohi; Mohammad Chegini; Alexandre Drouin; Perouz Taslakian; Valentina Zantedeschi; Alexandre Lacoste; David Vázquez 001; Nicolas Chapados; Christopher Pal; et al. (ICLR, 2025)

ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval. - Shubham Gupta; Zichao Li; Tianyi Chen; Cem Subakan; Siva Reddy; Perouz Taslakian; Valentina Zantedeschi (CoRR, 2025)

Learning to Defer for Causal Discovery with Imperfect Experts. - Oscar Clivio; Divyat Mahajan; Perouz Taslakian; Sara Magliacane; Ioannis Mitliagkas; Valentina Zantedeschi; Alexandre Drouin (CoRR, 2025)

PairBench: A Systematic Framework for Selecting Reliable Judge VLMs. - Aarash Feizi; Sai Rajeswar; Adriana Romero-Soriano; Reihaneh Rabbany; Spandana Gella; Valentina Zantedeschi; João Monteiro 002 (CoRR, 2025)

Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs. - Arjun Ashok; Andrew Robert Williams; Vincent Zhihao Zheng; Irina Rish; Nicolas Chapados; Étienne Marcotte; Valentina Zantedeschi; Alexandre Drouin (CoRR, 2025)