Latent Gaussian Process for Spike Data


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While I’m no longer able to participate in the Statistical Learning Reading Group at Michigan, I was a huge fan of reading the articles each week and thought I would post the slides of the articles I presented here in case anyone happens to find themselves interested.
While I don’t mention it in these slides specifically, I remember there being a widespread concern about the likely non-identifiability of the recovered parameters in this model, a common issue in machine learning methods. While this wouldn’t be as big of a concern if the aim were prediction, the authors did have the inference of the latent gaussian process as one of their inferential goals. Still, a very stimulating paper overall. It was originally presented at NIPS in 2018. The slides are linked here. and the pdf can be found here.

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Adam Peterson
Graduate Research Assistant

Adam Peterson is Biostatistics PhD candidate at the University of Michigan.