6 citations · 27 across the 13 of their papers we have counts for
13 papers
BIG-Bench Extra Hard
Mehran Kazemi, Bahare Fatemi, Hritik Bansal +17
Large language models (LLMs) are increasingly deployed in everyday applications, demanding robust general reasoning capabilities and diverse reasoning skillset. However, current LL…
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
Eleni Sgouritsa, Virginia Aglietti, Yee Whye Teh +3
The reasoning abilities of Large Language Models (LLMs) are attracting increasing attention. In this work, we focus on causal reasoning and address the task of establishing causal…
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
Virginia Aglietti, Ira Ktena, Jessica Schrouff +5
The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations. The be…
Additive Causal Bandits with Unknown Graph
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 learn…
Functional Causal Bayesian Optimization
Limor Gultchin, Virginia Aglietti, Alexis Bellot +1
We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph. fCBO extends the CBO family o…
Constrained Causal Bayesian Optimization
Virginia Aglietti, Alan Malek, Ira Ktena +1
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. cC…
Causal Entropy Optimization
Nicola Branchini, Virginia Aglietti, Neil Dhir +1
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. This problem arises in many a…
Dynamic Causal Bayesian Optimization
Virginia Aglietti, Neil Dhir, Javier González +1
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…
A variational Bayesian spatial interaction model for estimating revenue and demand at business facilities
Shanaka Perera, Virginia Aglietti, Theodoros Damoulas
We study the problem of estimating potential revenue or demand at business facilities and understanding its generating mechanism. This problem arises in different fields such as op…
Multi-task Causal Learning with Gaussian Processes
Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez +1
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. This is usefu…
Causal Bayesian Optimization
Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes +1
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. This problem aris…
Structured Variational Inference in Continuous Cox Process Models
Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas +1
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…
Efficient Inference in Multi-task Cox Process Models
Virginia Aglietti, Theodoros Damoulas, Edwin Bonilla
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGCPs, whose l…