no code implementations • 3 Sep 2024 • Filippo Aglietti, Francesco Della Santa, Andrea Piano, Virginia Aglietti
We propose Gradient Informed Neural Networks (GradINNs), a methodology inspired by Physics Informed Neural Networks (PINNs) that can be used to efficiently approximate a wide range of physical systems for which the underlying governing equations are completely unknown or cannot be defined, a condition that is often met in complex engineering problems.
no code implementations • 7 Jun 2024 • Virginia Aglietti, Ira Ktena, Jessica Schrouff, Eleni Sgouritsa, Francisco J. R. Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa
The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations.
1 code implementation • 13 Jun 2023 • Alan Malek, Virginia Aglietti, Silvia Chiappa
We explore algorithms to select actions in the causal bandit setting where the learner can choose to intervene on a set of random variables related by a causal graph, and the learner sequentially chooses interventions and observes a sample from the interventional distribution.
no code implementations • 10 Jun 2023 • Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa
We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph.
1 code implementation • 31 May 2023 • Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa
We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints.
no code implementations • 23 Aug 2022 • Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas
We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed.
1 code implementation • NeurIPS 2021 • Virginia Aglietti, Neil Dhir, Javier González, Theodoros Damoulas
This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time.
no code implementations • 5 Aug 2021 • Shanaka Perera, Virginia Aglietti, Theodoros Damoulas
We demonstrate how BSIM outperforms competing approaches on this large dataset in terms of prediction performances while providing results that are both interpretable and consistent with related indicators observed for the London region.
1 code implementation • NeurIPS 2020 • Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez, Javier González
This paper studies the problem of learning the correlation structure of a set of intervention functions defined on the directed acyclic graph (DAG) of a causal model.
no code implementations • 24 May 2020 • Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González
This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed.
1 code implementation • NeurIPS 2019 • Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by a continuous sigmoidal Cox process that assumes the corresponding intensity function is given by a Gaussian process (GP) prior transformed with a scaled logistic sigmoid function.
1 code implementation • 24 May 2018 • Virginia Aglietti, Theodoros Damoulas, Edwin Bonilla
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly.