On Contrastive Learning for Likelihood-free Inference

Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class of methods for this likelihood-free problem uses a classifier to distinguish between pairs of parameter-observation samples generated using the simulator and pairs sampled from some reference distribution, which implicitly learns a density ratio proportional to the likelihood. Another popular class of methods fits a conditional distribution to the parameter posterior directly, and a particular recent variant allows for the use of flexible neural density estimators for this task. In this work, we show that both of these approaches can be unified under a general contrastive learning scheme, and clarify how they should be run and compared.Comment: Appeared at ICML 202

"Getting the Ball Moving":An Evaluation of Fresh Start Through Sport 2023-24

In September 2023, Ulster University (UU) was commissioned by the Irish Football Association (IFA), on behalf of the Department for Communities, to conduct a continued evaluation of the Fresh Start Through Sport (FSTS) project for the 2023- 24 delivery period. This evaluation builds upon previous evaluations conducted for the 2020-21, 2021-22, and 2022-23 iterations of FSTS. The evaluation was carried out with the support of public agencies, community organisations, and key partners, including the Irish Football Association (IFA), Belfast Giants, Ulster Rugby, and the Gaelic Athletic Association (GAA). Facilitators and organisers from these groups offered input on the evaluation's direction and participated in in-depth semi- structured interviews. Additionally, young people who took part in FSTS shared their experiences of the project during focus groups. The core research team consisted of Dr Brendan Coyle, Dr Conor Murray, Mr Tobias Niblock, and Dr Colm Walsh. This report explores each core objective of the programme and research, highlights examples of good practice along with potential areas for improvement, and proposes a series of recommendations to feed into future iterations of the programme