90 research outputs found
Learning Rate Free Bayesian Inference in Constrained Domains
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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