Virginia Aglietti

27 papers A* 6A 4Journal 16Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Gradient-informed neural networks: Embedding prior beliefs for learning in low-data scenarios. Neural Networks Filippo Aglietti, Francesco Della Santa, Andrea Piano, Virginia Aglietti
2026	J	`jnl`	Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization. CoRR Felipe Maia Polo, Aida Nematzadeh, Virginia Aglietti, Adam Fisch, Isabela Albuquerque
2025	—	`conf`	BIG-Bench Extra Hard. ACL (1) 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
2025	J	`jnl`	BIG-Bench Extra Hard. CoRR 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
2025	A*	`conf`	FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch. ICML Virginia Aglietti, Ira Ktena, Jessica Schrouff, Eleni Sgouritsa, Francisco J. R. Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa
2024	J	`jnl`	FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch. CoRR Virginia Aglietti, Ira Ktena, Jessica Schrouff, Eleni Sgouritsa, Francisco J. R. Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa
2024	J	`jnl`	GradINN: Gradient Informed Neural Network. CoRR Filippo Aglietti, Francesco Della Santa, Andrea Piano, Virginia Aglietti
2024	J	`jnl`	Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation. CoRR Eleni Sgouritsa, Virginia Aglietti, Yee Whye Teh, Arnaud Doucet, Arthur Gretton, Silvia Chiappa
2023	A*	`conf`	Additive Causal Bandits with Unknown Graph. ICML Alan Malek, Virginia Aglietti, Silvia Chiappa
2023	J	`jnl`	Additive Causal Bandits with Unknown Graph. CoRR Alan Malek, Virginia Aglietti, Silvia Chiappa
2023	A	`conf`	Causal Entropy Optimization. AISTATS Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas
2023	A*	`conf`	Constrained Causal Bayesian Optimization. ICML Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa
2023	J	`jnl`	Constrained Causal Bayesian Optimization. CoRR Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa
2023	J	`jnl`	Functional Causal Bayesian Optimization. CoRR Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa
2023	A	`conf`	Functional causal Bayesian optimization. UAI Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa
2022	J	`jnl`	Causal Entropy Optimization. CoRR Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas
2021	J	`jnl`	A variational Bayesian spatial interaction model for estimating revenue and demand at business facilities. CoRR Shanaka Perera, Virginia Aglietti, Theodoros Damoulas
2021	A*	`conf`	Dynamic Causal Bayesian Optimization. NeurIPS Virginia Aglietti, Neil Dhir, Javier González, Theodoros Damoulas
2021	J	`jnl`	Dynamic Causal Bayesian Optimization. CoRR Virginia Aglietti, Neil Dhir, Javier González, Theodoros Damoulas
2020	A	`conf`	Causal Bayesian Optimization. AISTATS Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González
2020	J	`jnl`	Causal Bayesian Optimization. CoRR Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González
2020	A*	`conf`	Multi-task Causal Learning with Gaussian Processes. NeurIPS Virginia Aglietti, Theodoros Damoulas, Mauricio A. Álvarez, Javier González
2020	J	`jnl`	Multi-task Causal Learning with Gaussian Processes. CoRR Virginia Aglietti, Theodoros Damoulas, Mauricio A. Álvarez, Javier González
2019	A	`conf`	Efficient Inference in Multi-task Cox Process Models. AISTATS Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla
2019	A*	`conf`	Structured Variational Inference in Continuous Cox Process Models. NeurIPS Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
2019	J	`jnl`	Structured Variational Inference in Continuous Cox Process Models. CoRR Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
2018	J	`jnl`	Log Gaussian Cox Process Networks. CoRR Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla