#110 Unpacking Bayesian Methods in AI with Sam Duffield
Learning Bayesian Statistics
Advantages of Stochastic Gradient Markov Chain Monte Carlo (SGMCMC)
Exploring the benefits of using Stochastic Gradient Markov Chain Monte Carlo (SGMCMC) methods over traditional MCMC algorithms like Stan or PyMC, especially in handling large datasets efficiently. The chapter compares SGMC and MCMC methods, focusing on the advantages and limitations of stochastic gradient in approximating gradients for Bayesian techniques and discussing the impact of mini-batching on efficiency and unbiasedness.
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Takeaways:
- Use mini-batch methods to efficiently process large datasets within Bayesian frameworks in enterprise AI applications.
- Apply approximate inference techniques, like stochastic gradient MCMC and Laplace approximation, to optimize Bayesian analysis in practical settings.
- Explore thermodynamic computing to significantly speed up Bayesian computations, enhancing model efficiency and scalability.
- Leverage the Posteriors python package for flexible and integrated Bayesian analysis in modern machine learning workflows.
- Overcome challenges in Bayesian inference by simplifying complex concepts for non-expert audiences, ensuring the practical application of statistical models.
- Address the intricacies of model assumptions and communicate effectively to non-technical stakeholders to enhance decision-making processes.
Chapters:
00:00 Introduction to Large-Scale Machine Learning
11:26 Scalable and Flexible Bayesian Inference with Posteriors
25:56 The Role of Temperature in Bayesian Models
32:30 Stochastic Gradient MCMC for Large Datasets
36:12 Introducing Posteriors: Bayesian Inference in Machine Learning
41:22 Uncertainty Quantification and Improved Predictions
52:05 Supporting New Algorithms and Arbitrary Likelihoods
59:16 Thermodynamic Computing
01:06:22 Decoupling Model Specification, Data Generation, and Inference
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan and Francesco Madrisotti.
Links from the show:
- Sam on Twitter: https://x.com/Sam_Duffield
- Sam on Scholar: https://scholar.google.com/citations?user=7wm_ka8AAAAJ&hl=en&oi=ao
- Sam on Linkedin: https://www.linkedin.com/in/samduffield/
- Sam on GitHub: https://github.com/SamDuffield
- Posteriors paper (new!): https://arxiv.org/abs/2406.00104
- Blog post introducing Posteriors: https://blog.normalcomputing.ai/posts/introducing-posteriors/posteriors.html
- Posteriors docs: https://normal-computing.github.io/posteriors/
- Paper introducing Posteriors – Scalable Bayesian Learning with posteriors: https://arxiv.org/abs/2406.00104v1
- Normal Computing scholar: https://scholar.google.com/citations?hl=en&user=jGCLWRUAAAAJ&view_op=list_works
- Thermo blogs: https://blog.normalcomputing.ai/posts/2023-11-09-thermodynamic-inversion/thermo-inversion.html
- https://blog.normalcomputing.ai/posts/thermox/thermox.html
- Great paper on SGMCMC: https://proceedings.neurips.cc/paper_files/paper/2015/file/9a4400501febb2a95e79248486a5f6d3-Paper.pdf
- David MacKay textbook on Sustainable Energy: https://www.withouthotair.com/
- LBS #107 - Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/
- LBS #98 - Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/
Transcript
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