Preprints

Paper: arXiv:2608.25279. The previous post on this blog ended with an open problem. With Sonja Cox and Roy Schieven we had proved a sharp Wasserstein-to-total-variation regularization estimate for the OBABO … Read More

This work grew out of a particularly synergistic interdisciplinary collaboration with Massimo Solinas, Agnes Valenti, and Roeland Wiersema at the Center for Computational Quantum Physics, Flatiron Institute. A basic, but … Read More

I’m pleased to share a new paper with Siddharth Mitra and Andre Wibisono (both at Yale), titled: Tail-Sensitive KL and Rényi Convergence of Unadjusted Hamiltonian Monte Carlo via One-Shot Couplings. … Read More

How far can the ideas behind the No-U-Turn Sampler really go? We’ve been thinking about this question for some time, and it motivates recent work with my student Zichu Wang … Read More

How can we rigorously quantify Monte Carlo error and assess convergence in modern MCMC methods such as the No-U-Turn Sampler? This question motivates new joint work with Victor de la … Read More

In many Bayesian inference problems, the geometry of the posterior distribution can vary dramatically in scale. A classic example is Neal’s funnel, where the state-of-the-art algorithm, the No-U-Turn Sampler (NUTS), … Read More

Markov Chain Monte Carlo (MCMC) methods are fundamental for sampling from complex probability distributions, but many widely used algorithms either rely on gradients (like NUTS) and/or struggle with high-dimensional, multi-scale … Read More

Traditional methods like Gibbs sampling or randomized Kaczmarz rely heavily on specific coordinate systems, which can limit their efficiency—especially in ill-conditioned settings. But what happens when we step away from … Read More

The No-U-Turn Sampler (NUTS) is the go-to method for sampling in probabilistic programming languages like Stan, PyMC3, NIMBLE, Turing, and NumPyro. However, due to its recursive architecture, even proving its … Read More