3 papers
stat.ML2026
Mixed Poisson diffusion models and application to posterior sampling
Thibaut Pellerin, Alain Durmus, Elinor Berger +1
Diffusion models are a powerful framework for learning complex probability distributions. While widely used diffusion models for continuous data rely on Gaussian noising processes,…
stat.ML2023
General Graph Random Features
Isaac Reid, Krzysztof Choromanski, Eli Berger +1
We propose a novel random walk-based algorithm for unbiased estimation of arbitrary functions of a weighted adjacency matrix, coined universal graph random features (u-GRFs). This…
stat.ML2023
Repelling Random Walks
Isaac Reid, Eli Berger, Krzysztof Choromanski +1
We present a novel quasi-Monte Carlo mechanism to improve graph-based sampling, coined repelling random walks. By inducing correlations between the trajectories of an interacting e…