#139 Efficient Bayesian Optimization in PyTorch, with Max Balandat
Learning Bayesian Statistics
Exploring the Journey into Bayesian Optimization and Botarch
This chapter delves into Bayesian optimization using the Botarch tool, featuring a guest who shares their journey from electrical engineering to probabilistic modeling. The conversation highlights the intersection of theory and practical applications in the field of optimization.
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Takeaways:
- BoTorch is designed for researchers who want flexibility in Bayesian optimization.
- The integration of BoTorch with PyTorch allows for differentiable programming.
- Scalability at Meta involves careful software engineering practices and testing.
- Open-source contributions enhance the development and community engagement of BoTorch.
- LLMs can help incorporate human knowledge into optimization processes.
- Max emphasizes the importance of clear communication of uncertainty to stakeholders.
- The role of a researcher in industry is often more application-focused than in academia.
- Max's team at Meta works on adaptive experimentation and Bayesian optimization.
Chapters:
08:51 Understanding BoTorch
12:12 Use Cases and Flexibility of BoTorch
15:02 Integration with PyTorch and GPyTorch
17:57 Practical Applications of BoTorch
20:50 Open Source Culture at Meta and BoTorch's Development
43:10 The Power of Open Source Collaboration
47:49 Scalability Challenges at Meta
51:02 Balancing Depth and Breadth in Problem Solving
55:08 Communicating Uncertainty to Stakeholders
01:00:53 Learning from Missteps in Research
01:05:06 Integrating External Contributions into BoTorch
01:08:00 The Future of Optimization with LLMs
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, 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, 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, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, 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, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli and Adam Tilmar Jakobsen.
Links from the show:
- Max on Linkedin: https://www.linkedin.com/in/maximilian-balandat-b5843946/
- Max on GitHub: https://github.com/Balandat
- BoTorch – Bayesian Optimization in PyTorch: https://botorch.org/
- BoTorch – A Framework for Efficient Monte-Carlo Bayesian Optimization: https://arxiv.org/pdf/1910.06403
- Ax – A higher level, user-friendly black-box optimization tool that heavily leverages BoTorch: https://ax.dev/
- Ax – A Platform for Adaptive Experimentation: https://openreview.net/forum?id=U1f6wHtG1g#discussion
- PyTorch: https://docs.pytorch.org/docs/stable/index.html
- GPyTorch: https://gpytorch.ai/
Transcript
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