Events
Colloquium
Automatic Integration and Differentiation of Probabilistic Programs
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Speaker: Alexander Lew (Yale) Assistant Professor of Computer Science Yale University Wednesday, January 21, 2026 11:30AM - 1:00PM Lunch at 11:30am in 1307
Talk 12:00-1:00pm in 1327 Location: Yale Institute for Foundations of Data Science & Webcast, 219 Prospect Street, 13th Floor, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4cc83590-ff44-4425-b166-b3ca013d9fae |
Abstract: By automating the error-prone math behind deep learning, systems such as TensorFlow and PyTorch have supercharged machine learning research, empowering hundreds of thousands of practitioners to rapidly explore the design space of neural network architectures and training algorithms. This talk will show how new programming language techniques—particularly generalizations of automatic differentiation—make it possible to generalize and extend such systems to support probabilistic models. Our tools can automate the computation of expected values, probability densities, and their gradients, as well as help users derive fast, low-variance, unbiased estimators of these quantities when they are too expensive to compute exactly, enabling orders-of-magnitude speedups in downstream optimization and inference problems. To illustrate the value of these techniques, I’ll show how they have helped us build systems for (1) auditable reasoning and learning in relational domains, enabling the detection of thousands of errors across millions of Medicare records, and (2) probabilistic inference over large language models, enabling small open models to outperform frontier models on several constrained generation benchmarks.
Speaker Bio: Alex’s research aims to automate and scale up principled probabilistic reasoning, drawing on techniques from programming languages, machine learning, Bayesian statistics, and cognitive science. Alex is especially interested in the theory and practice of probabilistic and differentiable programming languages.
Alex is also a member of the GenLM consortium, a multi-university partnership aiming to better control, compose, and understand language models using the probabilistic programming and Bayesian inference toolkits.
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