Seminar 1:
Multifidelity Simulation-based Inference for Computationally Expensive Simulators
Abstract: Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of detail and accuracy, with models of high-fidelity (i.e., high accuracy) to the phenomena under study being often preferable. However, inferring parameters of high-fidelity models via simulation-based inference is challenging, especially when the simulator is computationally expensive. We introduce MF-NPE, a multifidelity approach to neural posterior estimation that leverages inexpensive low-fidelity simulations to infer parameters of high-fidelity simulators within a limited simulation budget. MF-NPE performs neural posterior estimation with limited high-fidelity resources by virtue of transfer learning, with the ability to prioritize individual observations using active learning. On one statistical task with analytical ground-truth and two real-world tasks, MF-NPE shows comparable performance to current approaches while requiring up to two orders of magnitude fewer high-fidelity simulations. Overall, MF-NPE opens new opportunities to perform efficient Bayesian inference on computationally expensive simulators.
About the Speaker: Anastasia Krouglova is a Ph.D. student at the interdisciplinary research centre NeuroElectronics Research Flanders (NERF) at KU Leuven. During her academic journey, she had the opportunity to undertake a research internship at ETH Zürich and to participate in an exchange program at Chalmers University of Technology. The focus of her current research lies in the field of computational neuroscience, which is the investment of brain function using mathematical tools and machine learning techniques.
