Today’s clip is from episode 139 of the podcast, with with Max Balandat.

Alex and Max discuss the integration of BoTorch with PyTorch, exploring its applications in Bayesian optimization and Gaussian processes. They highlight the advantages of using GPyTorch for structured matrices and the flexibility it offers for research.

The discussion also covers the motivations behind building BoTorch, the importance of open-source culture at Meta, and the role of PyTorch in modern machine learning.

Get the full discussion here.

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Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.