Additive Causal Bandits with Unknown Graph

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.

Functional Causal Bayesian Optimization

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.

Bayesian Optimization Gaussian Processes

Constrained Causal Bayesian Optimization

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.

Bayesian Optimization Gaussian Processes

Causal Entropy Optimization

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.

Bayesian Optimization

Dynamic Causal Bayesian Optimization

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.

Bayesian Optimization Causal Inference +1

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

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.

Decision Making Uncertainty Quantification +1

Multi-task Causal Learning with Gaussian Processes

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.

Active Learning Bayesian Optimization +2

Causal Bayesian Optimization

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.

Bayesian Optimization Causal Inference +2

Structured Variational Inference in Continuous Cox Process Models

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.

Numerical Integration Uncertainty Quantification +1

Efficient Inference in Multi-task Cox Process Models

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.

Gaussian Processes Point Processes +1