15 citations · 15 across the 5 of their papers we have counts for
6 papers
From Continuous Dynamics to Practical Gradient-Based Samplers
James Chok
Gradient-based Markov chain Monte Carlo methods are often introduced as a catalog of algorithms: Hamiltonian Monte Carlo (HMC), the Metropolis-adjusted Langevin algorithm (MALA), t…
Constrained Dikin-Langevin diffusion for polyhedra
James Chok, Domenic Petzinna
We propose a reflection-free Langevin framework for sampling and optimization on compact polyhedra. The method is based on the inverse Hessian of the logarithmic barrier, which def…
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…
Rational function approximation with normalized positive denominators
James Chok, Geoffrey M. Vasil
Recent years have witnessed the introduction and development of extremely fast rational function algorithms. Many ideas in this realm arose from polynomial-based linear-algebraic a…
Convex optimization over a probability simplex
James Chok, Geoffrey M. Vasil
We propose a new iteration scheme, the Cauchy-Simplex, to optimize convex problems over the probability simplex . Spe…
Geometric persistence and distributional trends in worldwide terrorism
Nick James, Max Menzies, James Chok +2
This paper introduces new methods for studying the prevalence of terrorism around the world and over time. Our analysis treats spatial prevalence of terrorism, the changing profile…