9 citations · 10 across the 4 of their papers we have counts for
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stat.ML2025
Sample-Efficient Optimisation over the Outputs of Generative Models
Samuel Willis, Paul Duckworth, Jack Simons +10
Modern generative AI models, such as diffusion and flow matching models, can sample from rich data distributions. However, many applications, especially in science and engineering,…
stat.ML2023★ 1 cited
Minimizing -Divergences by Interpolating Velocity Fields
Song Liu, Jiahao Yu, Jack Simons +2
Many machine learning problems can be seen as approximating a \textit{target} distribution using a \textit{particle} distribution by minimizing their statistical discrepancy. Wasse…
stat.ML2022★ 9 cited
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
Louis Sharrock, Jack Simons, Song Liu +1
We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models. Our method, inspired by the remarkable suc…