Valentina Zantedeschi

52 papers A* 11A 3Journal 32Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Bound to Disagree: Generalization Bounds via Certifiable Surrogates. CoRR Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain
2026	J	`jnl`	Overcoming the Modality Gap in Context-Aided Forecasting. CoRR Vincent Zhihao Zheng, Étienne Marcotte, Arjun Ashok, Andrew Robert Williams, Lijun Sun, Alexandre Drouin, Valentina Zantedeschi
2025	J	`jnl`	Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs. CoRR Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin
2025	A*	`conf`	Context is Key: A Benchmark for Forecasting with Essential Textual Information. ICML Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin
2025	A*	`conf`	InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation. ICLR Gaurav Sahu, Abhay Puri, Juan A. Rodríguez, Amirhossein Abaskohi, Mohammad Chegini, Alexandre Drouin, Perouz Taslakian, Valentina Zantedeschi, Alexandre Lacoste, David Vázquez, Nicolas Chapados, Christopher Pal, Sai Rajeswar, Issam H. Laradji
2025	J	`jnl`	Learning to Defer for Causal Discovery with Imperfect Experts. CoRR Oscar Clivio, Divyat Mahajan, Perouz Taslakian, Sara Magliacane, Ioannis Mitliagkas, Valentina Zantedeschi, Alexandre Drouin
2025	J	`jnl`	PairBench: A Systematic Framework for Selecting Reliable Judge VLMs. CoRR Aarash Feizi, Sai Rajeswar, Adriana Romero-Soriano, Reihaneh Rabbany, Spandana Gella, Valentina Zantedeschi, João Monteiro
2025	J	`jnl`	ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval. CoRR Shubham Gupta, Zichao Li, Tianyi Chen, Cem Subakan, Siva Reddy, Perouz Taslakian, Valentina Zantedeschi
2025	A	`conf`	Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses. AISTATS Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain
2024	J	`jnl`	Context is Key: A Benchmark for Forecasting with Essential Textual Information. CoRR Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin
2024	J	`jnl`	InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation. CoRR Gaurav Sahu, Abhay Puri, Juan A. Rodríguez, Alexandre Drouin, Perouz Taslakian, Valentina Zantedeschi, Alexandre Lacoste, David Vázquez, Nicolas Chapados, Christopher Pal, Sai Rajeswar, Issam Hadj Laradji
2024	A	`conf`	Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. AISTATS Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi
2024	J	`jnl`	Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. CoRR Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi
2024	J	`jnl`	Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones. CoRR Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas, João Monteiro, Valentina Zantedeschi
2024	A*	`conf`	RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content. NeurIPS João Monteiro, Pierre-André Noël, Étienne Marcotte, Sai Rajeswar Mudumba, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Chris Pal, Perouz Taslakian
2024	J	`jnl`	RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content. CoRR João Monteiro, Pierre-André Noël, Étienne Marcotte, Sai Rajeswar, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
2024	J	`jnl`	Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses. CoRR Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain
2024	A*	`conf`	TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series. ICLR Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin
2024	—	`conf`	XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference. EMNLP (Findings) João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
2024	J	`jnl`	XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference. CoRR João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
2023	J	`jnl`	Capture the Flag: Uncovering Data Insights with Large Language Models. CoRR Issam H. Laradji, Perouz Taslakian, Sai Rajeswar, Valentina Zantedeschi, Alexandre Lacoste, Nicolas Chapados, David Vázquez, Christopher Pal, Alexandre Drouin
2023	J	`jnl`	Causal Discovery with Language Models as Imperfect Experts. CoRR Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin
2023	A*	`conf`	DAG Learning on the Permutahedron. ICLR Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae
2023	J	`jnl`	DAG Learning on the Permutahedron. CoRR Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae
2023	A*	`conf`	Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. ICML Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados
2023	J	`jnl`	Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. CoRR Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados
2023	J	`jnl`	TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series. CoRR Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin
2022	J	`jnl`	Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery. NeuroImage 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
2022	J	`jnl`	Learning Discrete Directed Acyclic Graphs via Backpropagation. CoRR Andrew J. Wren, Pasquale Minervini, Luca Franceschi, Valentina Zantedeschi
2022	A*	`conf`	On Margins and Generalisation for Voting Classifiers. NeurIPS Felix Biggs, Valentina Zantedeschi, Benjamin Guedj
2022	J	`jnl`	On Margins and Generalisation for Voting Classifiers. CoRR Felix Biggs, Valentina Zantedeschi, Benjamin Guedj
2021	A*	`conf`	Learning Binary Decision Trees by Argmin Differentiation. ICML Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
2021	A*	`conf`	Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. NeurIPS Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj
2021	J	`jnl`	Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. CoRR Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj
2021	A*	`conf`	RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery. AAAI Christian Schröder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Alfredo Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski
2021	J	`jnl`	Unsupervised Change Detection of Extreme Events Using ML On-Board. CoRR Vít Ruzicka, Anna Vaughan, Daniele De Martini, James Fulton, Valentina Salvatelli, Chris Bridges, Gonzalo Mateo-Garcia, Valentina Zantedeschi
2020	A	`conf`	Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. AISTATS Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi
2020	—	`conf`	Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting. ECML/PKDD (3) Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi
2020	J	`jnl`	Learning Binary Trees via Sparse Relaxation. CoRR Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
2020	J	`jnl`	RainBench: Towards Global Precipitation Forecasting from Satellite Imagery. CoRR Christian Schröder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski
2019	J	`jnl`	Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph. CoRR Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi
2019	J	`jnl`	Cumulo: A Dataset for Learning Cloud Classes. CoRR Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris
2019	J	`jnl`	Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. CoRR Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi
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	J	`jnl`	Adversarial Robustness Toolbox v0.2.2. CoRR Maria-Irina Nicolae, Mathieu Sinn, Tran Ngoc Minh, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Ian M. Molloy, Benjamin Edwards
2018	—	`conf`	Fast and Provably Effective Multi-view Classification with Landmark-Based SVM. ECML/PKDD (2) Valentina Zantedeschi, Rémi Emonet, Marc Sebban
2017	—	`conf`	Efficient Defenses Against Adversarial Attacks. AISec@CCS Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat
2017	J	`jnl`	Efficient Defenses Against Adversarial Attacks. CoRR Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat
2017	J	`jnl`	L CoRR Valentina Zantedeschi, Rémi Emonet, Marc Sebban
2016	J	`jnl`	Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms. CoRR Valentina Zantedeschi, Rémi Emonet, Marc Sebban
2016	A*	`conf`	Metric Learning as Convex Combinations of Local Models with Generalization Guarantees. CVPR Valentina Zantedeschi, Rémi Emonet, Marc Sebban
2016	—	`conf`	beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data. NIPS Valentina Zantedeschi, Rémi Emonet, Marc Sebban