On Couplings for Kinetic Langevin Diffusions
Joint work with Sonja Cox and Roy Schieven (University of Amsterdam). Paper: arXiv:2605.31088. Two copies of the same diffusion, started apart. How can you get them to coalesce? For kinetic … Read More
Blog
New paper: Neural Quantum States in Mixed Precision
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
Tail-Sensitive Convergence Guarantees for Unadjusted Hamiltonian Monte Carlo
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
Extending No-U-Turn Ideas to Discrete State Spaces
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
Decoupling for Markov Chains
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
Stochastic Computation Workshop at FoCM 2026 – Vienna, Austria
📅 Dates: July 13–15, 2026 📍 Location: Vienna, Austria 🔗 Workshop Information 🎲 I’m excited to be co-organizing the Stochastic Computation Workshop at FoCM 2026, alongside Mireille Bossy and David … Read More
WALNUTS = Within-orbit Adaptive Leapfrog No-U-Turn Sampler
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
A Locally Adaptive, Gradient-Free MCMC Method Inspired by the No-U-Turn Sampler
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
Why Go Coordinate-Free in Monte Carlo and Optimization?
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
Watch My Latest Talk on the No-U-Turn Sampler
Curious about the No-U-Turn Sampler and its performance in high-dimensional spaces? Watch my recent talk, where I present new insights into its reversibility and mixing time. There are still many … Read More