Max Balandat

In this discussion, Max Balandat, a key figure in Bayesian optimization and an advocate for open-source culture at Meta, shares insights on the integration of BoTorch with PyTorch. He highlights the flexibility and user-friendly nature of GPyTorch for handling optimization challenges with large datasets. Max explores the advantages of using neural networks as feature extractors in high-dimensional Bayesian optimization and emphasizes the importance of open-source collaboration in advancing research in this dynamic field.

Max Balandat, who leads the modeling and optimization team at Meta, discusses the fascinating world of Bayesian optimization and the BoTorch library. He shares insights on the seamless integration of BoTorch with PyTorch, enhancing flexibility for researchers. The conversation delves into the significance of adaptive experimentation and the impact of LLMs on optimization. Max emphasizes the importance of effectively communicating uncertainty to stakeholders and reflects on the transition from academia to industry, highlighting collaboration in research.