is it necessary to learn summary statistics? [One World ABC seminar]

Next week, on 30 November, at 9am (UK time), Yanzhi Chen (Cambridge) will give a One World ABC webinar on Is “It Necessary to Learn Summary Statistics for Likelihood-free Inference?”, a PMLR paper join with Michael Guttman and Adrian Weller:

Likelihood-free inference (LFI) is a set of techniques for inference in implicit statistical models. A longstanding question in LFI has been how to design or learn good summary statistics of data, but this might now seem unnecessary due to the advent of recent end-to- end (i.e. neural network-based) LFI methods. In this work, we rethink this question with a new method for learning summary statistics. We show that learning sufficient statistics may be easier than direct posterior inference, as the former problem can be reduced to a set of low-dimensional, easy-to-solve learning problems. This suggests us to explicitly decouple summary statistics learning from posterior inference in LFI. Experiments on five inference tasks with different data types validate our hypothesis.