Valentina Zantedeschi

According to our database1, Valentina Zantedeschi authored at least 32 papers between 2016 and 2024.

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Bibliography

2024
Context is Key: A Benchmark for Forecasting with Essential Textual Information.
CoRR, 2024

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation.
CoRR, 2024

RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content.
CoRR, 2024

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Capture the Flag: Uncovering Data Insights with Large Language Models.
CoRR, 2023

Causal Discovery with Language Models as Imperfect Experts.
CoRR, 2023

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts.
Proceedings of the International Conference on Machine Learning, 2023

DAG Learning on the Permutahedron.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery.
NeuroImage, 2022

Learning Discrete Directed Acyclic Graphs via Backpropagation.
CoRR, 2022

On Margins and Generalisation for Voting Classifiers.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Unsupervised Change Detection of Extreme Events Using ML On-Board.
CoRR, 2021

Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Binary Decision Trees by Argmin Differentiation.
Proceedings of the 38th International Conference on Machine Learning, 2021

RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery.
CoRR, 2020

Learning Binary Trees via Sparse Relaxation.
CoRR, 2020

Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Cumulo: A Dataset for Learning Cloud Classes.
CoRR, 2019

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting.
CoRR, 2019

Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph.
CoRR, 2019

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).
PhD thesis, 2018

Adversarial Robustness Toolbox v0.2.2.
CoRR, 2018

Fast and Provably Effective Multi-view Classification with Landmark-Based SVM.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

2017
L<sup>3</sup>-SVMs: Landmarks-based Linear Local Support Vectors Machines.
CoRR, 2017

Efficient Defenses Against Adversarial Attacks.
Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017

2016
Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms.
CoRR, 2016

beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Metric Learning as Convex Combinations of Local Models with Generalization Guarantees.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016


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