@inproceedings{mackey2023learning,
author = {Mackey, Lester and Sharrock, Louis and Nemeth, Christopher},
title = {Learning Rate Free Bayesian Inference in Constrained Domains},
booktitle = {NeurIPS 2023},
year = {2023},
month = {May},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-rate-free-bayesian-inference-in-constrained-domains/},
}