8 papers
Training-Free Generative Sampling via Moment-Matched Score Smoothing
Zhenyu Yao, Daniel Paulin
Diffusion models generate samples by denoising along the score of a perturbed target distribution. In practice, one trains a neural diffusion model, which is computationally expens…
Divide, Interact, Sample: The Two-System Paradigm
James Chok, Myung Won Lee, Daniel Paulin +1
Mean-field, ensemble-chain, and adaptive samplers have historically been viewed as distinct approaches to Monte Carlo sampling. In this paper, we present a unifying {two-system} fr…
Theoretical guarantees for stochastic gradient sampling methods via Gaussian convolution inequalities
Daniel Paulin, Peter A. Whalley
We derive first-order (in the stepsize) bounds on the bias in Wasserstein distances of the invariant measure of stochastic gradient kinetic Langevin dynamics with minimal assumptio…
A Semiparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior
Trinnhallen Brisley, Gordon Ross, Daniel Paulin
Hawkes processes are used in settings where past events increase the likelihood of future events occurring, resulting in a natural clustering structure. Traditional Hawkes process…
Infinite-dimensional generative diffusions via Doob's h-transform
Thorben Pieper-Sethmacher, Daniel Paulin
This paper introduces a rigorous framework for defining generative diffusion models in infinite dimensions via Doob's h-transform. Rather than relying on time reversal of a noising…
Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
Neil K. Chada, Benedict Leimkuhler, Daniel Paulin +1
We present an unbiased method for Bayesian posterior means based on kinetic Langevin dynamics that combines advanced splitting methods with enhanced gradient approximations. Our ap…