Jianfeng Lu

activity

20172026

most citedDiscontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods

56 citations · 58 across the 15 of their papers we have counts for

collaborators

18 papers

math.ST2026

Sharp dimensional analysis of midpoint methods for Langevin sampling

Fan Chen, Sinho Chewi, Jianfeng Lu +1

We study deterministic and randomized midpoint discretizations of Langevin dynamics for a target $π \propto e^{- V}$ , where $0 ≺ α I ⪯ \nabla^{2} V ⪯ β I$ and $κ = β / α$ . To achi…

math.ST2026

High-accuracy simulation of Picard HMC, part I: Gaussian cloud correction

Fan Chen, Sinho Chewi, Jianfeng Lu +1

We study the problem of sampling from a continuous density $π \propto exp (- V)$ on $R^{d}$ , where $V \in C^{2} (R^{d})$ has a $β$ -Lipschitz gradient and $π$ satisfies a loga…

math.PR2026

Sharp Gaussian divergence and Bernstein-Markov inequalities

Fan Chen, Sinho Chewi, Jianfeng Lu +1

We prove two dimension-free estimates in Gaussian space. The first is an optimal Meyer-type inequality for the Gaussian divergence: for $p \geq 2$ , \[ \|δV\|_{L^p(γ_d)} \leq \sqrt p\…

math.ST2026

Smoothed Picard Hamiltonian Monte Carlo

Fan Chen, Sinho Chewi, Jianfeng Lu +1

We develop a new low-accuracy sampler, called smoothed Picard Hamiltonian Monte Carlo, which combines Gaussian smoothing, Picard iteration, and higher-order discretization. For a l…

math.ST2026

Accelerated High-Accuracy Sampling from a Warm Start via the Proximal Bouncy Particle Sampler

Fan Chen, Sinho Chewi, Jianfeng Lu +1

We study the problem of sampling from $μ (d x) \propto e^{- V (x)} d x$ on $R^{d}$ , where $V$ is $α$ -strongly convex and $β$ -smooth, and write $κ := β / α$ . We desi…

math.NA2026

Windowed thinning and query complexity for the bouncy particle and Zigzag samplers

Jianfeng Lu, Yinchen Luo

Let $μ (d x) \propto e^{- U (x)} d x$ on $R^{d}$ , where $U$ is $m$ -strongly convex and $L$ -smooth, and denote by $κ = L / m$ the condition number. We consider windowed thinning, an exact si…

math.PR2025

Long-time reverse transportation inequalities for non-globally-dissipative Langevin dynamics

Jianfeng Lu, Yuliang Wang

We establish a dimension-free, uniform-in-time reverse transportation inequality for Langevin dynamics with non-convex potentials. This inequality controls the Rényi divergence of…

math.NA2025

Approximating Young Measures With Deep Neural Networks

Rayehe Karimi Mahabadi, Jianfeng Lu, Hossein Salahshoor

Parametrized measures (or Young measures) enable to reformulate non-convex variational problems as convex problems at the cost of enlarging the search space from space of functions…

math.AP2025

Quantitative Hypocoercivity and Lifting of Classical and Quantum Dynamics

Jianfeng Lu

We consider quantitative convergence analysis for hypocoercive dynamics such as Langevin and Lindblad equations describing classical and quantum open systems. Our goal is to provid…

quant-ph2025

Quantum Fisher information matrix via its classical counterpart from random measurements

Jianfeng Lu, Kecen Sha

Preconditioning with the quantum Fisher information matrix (QFIM) is a popular approach in quantum variational algorithms. Yet the QFIM is costly to obtain directly, usually requir…

math.AP2025

Solution Theory of Hamilton-Jacobi-Bellman Equations in Spectral Barron Spaces

Ye Feng, Jianfeng Lu

We study the solution theory of the whole-space static (elliptic) Hamilton-Jacobi-Bellman (HJB) equation in spectral Barron spaces. We prove that under the assumption that the coef…

cs.LG2025

Bi-Lipschitz Ansatz for Anti-Symmetric Functions

Nadav Dym, Jianfeng Lu, Matan Mizrachi

Motivated by applications to the simulation of quantum many-body systems by neural networks, researchers have suggested several models which are antisymmetric by construction, and…

cs.LG2024

What does guidance do? A fine-grained analysis in a simple setting

Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen +2

The use of guidance in diffusion models was originally motivated by the premise that the guidance-modified score is that of the data distribution tilted by a conditional likelihood…

stat.CO2024

Guidance for twisted particle filter: a continuous-time perspective

Jianfeng Lu, Yuliang Wang

The particle filter (PF), also known as sequential Monte Carlo (SMC), approximates high-dimensional probability distributions and their normalizing constants in the discrete-time s…

math.NA2024★ 1 cited

Error Bounds for Open Quantum Systems with Harmonic Bosonic Bath

Kaizhao Liu, Jianfeng Lu

We investigate the dependence of physical observable of open quantum systems with Bosonic bath on the bath correlation function. We provide an error estimate of the difference of p…

physics.comp-ph2023★ 1 cited

Diffusion Methods for Generating Transition Paths

Luke Triplett, Jianfeng Lu

In this work, we seek to simulate rare transitions between metastable states using score-based generative models. An efficient method for generating high-quality transition paths i…

math.NA2023

Riemannian Langevin Monte Carlo schemes for sampling PSD matrices with fixed rank

Tianmin Yu, Shixin Zheng, Jianfeng Lu +2

This paper introduces two explicit schemes to sample matrices from Gibbs distributions on $S_{+}^{n, p}$ , the manifold of real positive semi-definite (PSD) matrices of size $…

stat.CO2017★ 56 cited

Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods

Akihiko Nishimura, David Dunson, Jianfeng Lu

Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation. In this article, we present an extension that can efficiently explore target distributions with di…