6 citations · 20 across the 8 of their papers we have counts for
10 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…
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…