From the 1 of 5 linked papers with an AI index.
5 papers
Delocalization of bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevin
Yifan Chen, Xiaoou Cheng, Jonathan Niles-Weed +1
The paper analyzes the bias of unadjusted Hamiltonian Monte Carlo and underdamped Langevin samplers, showing that controlling the Wasserstein‑2 bias of any marginal requires only O…
Composing diffusion priors with explicit physical context via generative Gibbs sampling
Weizhou Wang, Jonathan Weare, Aaron R. Dinner
Pretrained diffusion models provide powerful learned priors, but in scientific sampling the target distribution often depends on physical context that is not fully represented by o…
Functional Estimation of the Marginal Likelihood
Omiros Papaspiliopoulos, Timothée Stumpf-Fétizon, Jonathan Weare
We propose a framework for computing, optimizing and integrating with respect to a smooth marginal likelihood in statistical models that involve high-dimensional parameters/latent…
Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias
Yifan Chen, Xiaoou Cheng, Jonathan Niles-Weed +1
The unadjusted Langevin algorithm is commonly used to sample probability distributions in extremely high-dimensional settings. However, existing analyses of the algorithm for stron…
The surprising efficiency of temporal difference learning for rare event prediction
Xiaoou Cheng, Jonathan Weare
We quantify the efficiency of temporal difference (TD) learning over the direct, or Monte Carlo (MC), estimator for policy evaluation in reinforcement learning, with an emphasis on…