University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Stochastic Gradient Langevin Dynamics for Large Scale Bayesian Inference

Stochastic Gradient Langevin Dynamics for Large Scale Bayesian Inference

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Mustapha Amrani.

Advanced Monte Carlo Methods for Complex Inference Problems

The Bayesian approach to statistical machine learning is a theoretically well-motivated framework to learning from data. It provides a coherent framework to reasoning about uncertainties, and an inbuilt protection against overfitting. However, computations in the framework can be expensive, and most approaches to Bayesian computations do not scale well to the big data setting. In this talk we propose a new computational approach for Bayesian learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm we show that the iterates will converge to samples from the true posterior distribution as we anneal the stepsize. We apply the method to logistic regression and latent Dirichlet allocation, showing state-of-the-art performance.

Joint work with Max Welling and Sam Patterson.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity