#78 Exploring MCMC Sampler Algorithms, with Matt D. Hoffman
Learning Bayesian Statistics
The Biggest Herderal in the Bayesian Workflow?
The amount to which your tightly constrained degrees of freedom are constrained is going down as kind of the square root of the amount of data. So that's like, you know, there's that super linear cost that comes up. And in part, it's probably an algorithms thing that the algorithms that work well for the problems that we're studying are the models that have been successful. But I'll take better measurements over better modeling any day," he says.
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Matt Hoffman has already worked on many topics in his life – music information retrieval, speech enhancement, user behavior modeling, social network analysis, astronomy, you name it.
Obviously, picking questions for him was hard, so we ended up talking more or less freely — which is one of my favorite types of episodes, to be honest.
You’ll hear about the circumstances Matt would advise picking up Bayesian stats, generalized HMC, blocked samplers, why do the samplers he works on have food-based names, etc.
In case you don’t know him, Matt is a research scientist at Google. Before that, he did a postdoc in the Columbia Stats department, working with Andrew Gelman, and a Ph.D at Princeton, working with David Blei and Perry Cook.
Matt is probably best known for his work in approximate Bayesian inference algorithms, such as stochastic variational inference and the no-U-turn sampler, but he’s also worked on a wide range of applications, and contributed to software such as Stan and TensorFlow Probability.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
Thank you to my Patrons for making this episode possible!
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Links from the show:
- Matt’s website: http://matthewdhoffman.com/
- Matt on Google Scholar: https://scholar.google.com/citations?hl=en&user=IeHKeGYAAAAJ&view_op=list_works
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo: https://www.jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf
- Tuning-Free Generalized Hamiltonian Monte Carlo: https://proceedings.mlr.press/v151/hoffman22a/hoffman22a.pdf
- Nested R-hat: Assessing the convergence of Markov chain Monte Carlo when running many short chain: http://www.stat.columbia.edu/~gelman/research/unpublished/nestedRhat.pdf
- Automatic Reparameterisation of Probabilistic Programs: http://proceedings.mlr.press/v119/gorinova20a/gorinova20a.pdf
Abstract
written by Christoph Bamberg
In this episode, Matt D. Hoffman, a Google research scientist discussed his work on probabilistic sampling algorithms with me. Matt has a background in music information retrieval, speech enhancement, user behavior modeling, social network analysis, and astronomy.
He came to machine learning (ML) and computer science through his interest in synthetic music and later took a Bayesian modeling class during his PhD.
He mostly works on algorithms, including Markov Chain Monte Carlo (MCMC) methods that can take advantage of hardware acceleration, believing that running many small chains in parallel is better for handling autocorrelation than running a few longer chains.
Matt is interested in Bayesian neural networks but is also skeptical about their use in practice.
He recently contributed to a generalised Hamilton Monte Carlo (HMC) sampler, and previously worked on an alternative to the No-U-Turn-Sampler (NUTS) called MEADS. We discuss the applications for these samplers and how they differ from one another.
In addition, Matt introduces an improved R-hat diagnostic tool, nested R-hat, that he and colleagues developed.
Automated Transcript
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