Statistics Seminar: Calibration Methods for Generative Models with Application to Protein Structure Modeling

Presenter: Brian Trippe, Assistant Professor, Stanford University, Department of Statistics

Description: Generative models frequently suffer miscalibration, where the statistics of their generations deviate from desired values. Protein structure diffusion models, for example, produce highly realistic samples but often underestimate the probabilities of important modes. These deviations are not addressed by existing fine-tuning methods for image and text generation. We frame calibration of generative models as a constrained optimization problem that we seek to solve by fine-tuning. Because the natural objective is intractable, we introduce two surrogate objectives for which we can compute low-variance gradient estimates amenable to stochastic optimization. The resulting procedures reduce the majority of calibration error across hundreds of simultaneous constraints and models with up to nine billion parameters. Lastly, we describe an application for assimilating thermodynamic measurements to calibrate a generative model of protein structure Boltzmann ensembles.

About the speaker: Brian Trippe is an Assistant Professor in the Department of Statistics at Stanford. Previously he was a visiting researcher at the Institute for Protein Design at the University of Washington where he worked with David Baker, and a postdoctoral fellow at Columbia University. He completed his Ph.D. in Computational and Systems Biology at MIT in 2022. His recent research has developed statistical machine learning methods to address challenges in biotechnology and medicine, with a focus on generative modeling and inference algorithms for protein engineering.

This seminar is hosted by Professor Yunyi Shen.