Poster

Learning Rate Free Bayesian Inference in Constrained Domains

Louis Sharrock · Lester Mackey · Christopher Nemeth

Great Hall & Hall B1+B2 (level 1) #1221
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Tue 12 Dec 8:45 a.m. PST — 10:45 a.m. PST

Abstract:

We introduce a suite of new particle-based algorithms for sampling on constrained domains which are entirely learning rate free. Our approach leverages coin betting ideas from convex optimisation, and the viewpoint of constrained sampling as a mirrored optimisation problem on the space of probability measures. Based on this viewpoint, we also introduce a unifying framework for several existing constrained sampling algorithms, including mirrored Langevin dynamics and mirrored Stein variational gradient descent. We demonstrate the performance of our algorithms on a range of numerical examples, including sampling from targets on the simplex, sampling with fairness constraints, and constrained sampling problems in post-selection inference. Our results indicate that our algorithms achieve competitive performance with existing constrained sampling methods, without the need to tune any hyperparameters.

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