Proves Ω(√d) lower bound for PDMP samplers and presents a new adaptive PDMP achieving sub-√d empirical complexity O(d^α) (α∈[0.2,0.3]) for Gaussian-tailed targets.
Automated techniques for effi- cient sampling of piecewise-deterministic Markov processes.arXiv preprint arXiv:2408.03682, 2024
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Establishing an $\Omega(\sqrt{d})$ complexity lower bound for PDMP samplers and how to break it: a sub-$\sqrt{d}$ algorithm for Gaussian-tailed targets
Proves Ω(√d) lower bound for PDMP samplers and presents a new adaptive PDMP achieving sub-√d empirical complexity O(d^α) (α∈[0.2,0.3]) for Gaussian-tailed targets.