Fresh Start Through Sport 2023-2024 Addendum:The Ambassador Initiative

While the initial evaluation of FSTS in 2023-24 referenced the Ambassador initiative as a positive innovation, a full assessment of its impact was intentionally scoped for future research. This addendum delivers on that aim, presenting a narrative-based evaluation of the Ambassador initiative. Through in-depth, semi-structured interviews with five FSTS Ambassadors, this brief report explores the lived impact of the Ambassador initiative. The narratives in each case study present evidence of positive change across a range of domains, from improved mental health and mental health literacy, to employment, educational progression, and civic engagement. Collectively, the case studies make a robust case for the initiative’s continued support, development, and consistent integration into the wider FSTS programme and beyond

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

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