1 citations · 1 across the 3 of their papers we have counts for
4 papers
BayesAME: Bayesian Active Model Evaluation
Paula Cordero Encinar, Taylan Cemgil, Arnaud Doucet +2
Evaluating large generative models across benchmarks is time-consuming and computationally expensive. This drives the need for methods that can estimate full benchmark performance…
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…
Mind the Graph When Balancing Data for Fairness or Robustness
Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki +5
Failures of fairness or robustness in machine learning predictive settings can be due to undesired dependencies between covariates, outcomes and auxiliary factors of variation. A c…