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EC Bayes Seminar: "Non-reversible parallel tempering on optimized paths" Saifuddin Syed (University of British Columbia)

  • 11 Feb 2022 9:52 AM
    Message # 12591193


    We are delighted to have our first EC* Bayes Seminar for 2022 presented by Saifuddin Syed from the Department of Statistics at the University of British Columbia.

    Virtual attendees: A Zoom link will be emailed prior to event.

    Title: Non-reversible parallel tempering on optimized paths

    Abstract: MCMC methods are the most widely used tools in computation science used to evaluate expectations with respect to complex probability distributions over general state spaces. The work by averaging over the trajectory of a Markov chain stationary with respect to the target distribution. In theory, the MCMC algorithms converge asymptotically as the computation time goes arbitrary large. Still, in practice, for challenging problems, where the target distributions are high-dimensional with well-separated modes, MCMC algorithms can get trapped exploring local regions of high probability and suffer from poor mixing in a finite time.

    Physicists and statisticians independently introduced parallel tempering (PT) algorithms to tackle this issue. PT delegates the task of exploration to additional annealed chains running in parallel with better mixing properties. They then communicate with the target chain of interest and help it discover new unexplored regions of the sample space. Since their introduction in the ’90s, PT algorithms are still extensively used to improve mixing in challenging sampling problems arising in statistics, physics, computational chemistry, phylogenetics, and machine learning.

    The classical approach to designing PT algorithms was developed using a reversible paradigm that is difficult to tune and deteriorates in performance when too many parallel chains are introduced. This talk will introduce a new non-reversible paradigm for PT that dominates its reversible counterpart while avoiding the performance collapse endemic to reversible methods. We will then establish near-optimal tuning guidelines and efficient black-box methodology scalable to GPUs. Our work out-performs state-of-the-art PT methods and has been used at scale by researchers to study the evolutionary structure of cancer and discover magnetic polarization in the photograph of the supermassive black hole M87.

    Presented by the Centre for Data Science, Queensland University of Technology.

    *Early Career: We want to highlight the work of early career researchers as opportunities for ECRs to present their work to broad audiences has declined.

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