2024-04-18-chen24f.md

title	software	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Coreset Markov chain Monte Carlo	https://github.com/NaitongChen/coreset-mcmc-experiments	A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during inference in order to reduce computational cost. However, state of the art methods for tuning coreset weights are expensive, require nontrivial user input, and impose constraints on the model. In this work, we propose a new method—coreset MCMC—that simulates a Markov chain targeting the coreset posterior, while simultaneously updating the coreset weights using those same draws. Coreset MCMC is simple to implement and tune, and can be used with any existing MCMC kernel. We analyze coreset MCMC in a representative setting to obtain key insights about the convergence behaviour of the method. Empirical results demonstrate that coreset MCMC provides higher quality posterior approximations and reduced computational cost compared with other coreset construction methods. Further, compared with other general subsampling MCMC methods, we find that coreset MCMC has a higher sampling efficiency with competitively accurate posterior approximations.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chen24f	0	{C}oreset {M}arkov chain {M}onte {C}arlo	4438	4446	4438-4446	4438	false	Chen, Naitong and Campbell, Trevor	given family Naitong Chen given family Trevor Campbell	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/chen24f/chen24f.pdf