Martin Ingram
Researcher who worked on making variational inference more reliable, author of blog posts and papers on ADVI and DADVI, and contributor to methods like linear response covariance estimation for variational approximations.
Best podcasts with Martin Ingram
Ranked by the Snipd community
Dec 17, 2025 • 22min
BITESIZE | Making Variational Inference Reliable: From ADVI to DADVI
Martin Ingram, a researcher known for his work on reliable variational inference, shares valuable insights on ADVI and DADVI. He discusses the allure and pitfalls of ADVI, emphasizing tuning challenges and convergence issues. The conversation digs into the advantages and drawbacks of mean-field variational inference and introduces the innovative linear response technique for covariance estimation. Martin also contrasts stochastic and deterministic optimization, revealing how DADVI's fixed-draw method can enhance reliability while acknowledging the trade-offs involved.
Dec 12, 2025 • 1h 10min
#147 Fast Approximate Inference without Convergence Worries, with Martin Ingram
Martin Ingram, a data scientist and Bayesian researcher known for his work on DADVI and contributions to PyMC, dives into fast approximate inference methods. He discusses how DADVI enhances speed and accuracy in Bayesian inference while maintaining model flexibility. The conversation covers recovering covariance estimates using linear response and contrasts deterministic optimization with stochastic methods. Martin also shares insights on the practical performance of DADVI across different models and hints at exciting future enhancements like GPU support and exploring normalizing flows.
