Virginia Aglietti

According to our database, Virginia Aglietti authored at least 18 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization.

Felipe Maia Polo, Aida Nematzadeh, Virginia Aglietti, Adam Fisch, Isabela Albuquerque

CoRR, March, 2026

Gradient-informed neural networks: Embedding prior beliefs for learning in low-data scenarios.

Filippo Aglietti, Francesco Della Santa, Andrea Piano, Virginia Aglietti

Neural Networks, 2026

2025

BIG-Bench Extra Hard.

Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Kshitij Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V. Le, Orhan Firat

CoRR, February, 2025

FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch.

Virginia Aglietti, Ira Ktena, Jessica Schrouff, Eleni Sgouritsa, Francisco J. R. Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa

Proceedings of the Forty-second International Conference on Machine Learning, 2025

BIG-Bench Extra Hard.

Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K. Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V. Le, Orhan Firat

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation.

Eleni Sgouritsa, Virginia Aglietti, Yee Whye Teh, Arnaud Doucet, Arthur Gretton, Silvia Chiappa

CoRR, 2024

GradINN: Gradient Informed Neural Network.

Filippo Aglietti, Francesco Della Santa, Andrea Piano, Virginia Aglietti

CoRR, 2024

2023

Functional causal Bayesian optimization.

Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Additive Causal Bandits with Unknown Graph.

Alan Malek, Virginia Aglietti, Silvia Chiappa

Proceedings of the International Conference on Machine Learning, 2023

Constrained Causal Bayesian Optimization.

Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa

Proceedings of the International Conference on Machine Learning, 2023

Causal Entropy Optimization.

Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2021

A variational Bayesian spatial interaction model for estimating revenue and demand at business facilities.

Shanaka Perera, Virginia Aglietti, Theodoros Damoulas

CoRR, 2021

Dynamic Causal Bayesian Optimization.

Virginia Aglietti, Neil Dhir, Javier González, Theodoros Damoulas

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Multi-task Causal Learning with Gaussian Processes.

Virginia Aglietti, Theodoros Damoulas, Mauricio A. Álvarez, Javier González

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Bayesian Optimization.

Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Structured Variational Inference in Continuous Cox Process Models.

Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Efficient Inference in Multi-task Cox Process Models.

Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Log Gaussian Cox Process Networks.

Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla

CoRR, 2018