On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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48 results for “Hamiltonian Monte Carlo”

Antithetic Riemannian Manifold And Quantum-Inspired Hamiltonian Monte Carlostat.ML

New algorithms improve MCMC efficiency for complex distributions.

problem High variance and low effective sample size in MCMC samplers.

method Antithetic Riemannian Manifold and Quantum-Inspired Hamiltonian Monte Carlo.

result Improved effective sample size and variance reduction.

MCMC Hamiltonian Monte Carlo Riemannian Manifold Quantum-Inspired Antithetic Sampling

2021-07-05abs ↗pdf ↗

L^q

Convergence of the Hamiltonian Monte Carlomath.CA

Hamiltonian Monte Carlo converges to target distributions under mild conditions.

problem Establishing convergence of Hamiltonian Monte Carlo algorithms.

method Analyzing

L^q

convergence for Hamiltonian Monte Carlo under mild conditions.

result Outputs converge to target distributions under specified conditions.

Hamiltonian Monte Carlo convergence L^q target distribution

2021-01-21abs ↗pdf ↗

Deep Learning Hamiltonian Monte Carlohep-lat

Deep learning enhances Hamiltonian Monte Carlo for sampling gauge field configurations.

problem Sampling from complex gauge field topologies efficiently.

method Stacked neural networks to generalize Hamiltonian Monte Carlo.

result Significantly reduces computational cost for generating gauge field configurations.

Hamiltonian Monte Carlo Deep learning Gauge field theory Monte Carlo sampling Neural networks

2021-05-07abs ↗pdf ↗

An Introduction to Hamiltonian Monte Carlo Method for Samplingcs.DS

Introduces HMC method for sampling Gibbs densities.

problem Sampling from Gibbs densities efficiently.

method Hamiltonian Monte Carlo (HMC) method based on Hamiltonian dynamics.

result Idealized HMC preserves the target distribution and converges under certain conditions.

Hamiltonian Monte Carlo Gibbs sampling Markov Chain Monte Carlo Hamiltonian dynamics Gibbs density

2021-08-27abs ↗pdf ↗

Randomized Runge-Kutta-Nyström Methods for Unadjusted Hamiltonian and Kinetic Langevin Monte Carlomath.NA

New methods improve efficiency of sampling algorithms for complex systems.

problem Efficiently sampling from complex, high-dimensional probability distributions.

method Randomized Runge-Kutta-Nyström methods tailored for Hamiltonian flows.

result Quantitative

5/2

-order

L^2

-accuracy in approximating Hamiltonian flows.

Monte Carlo methods Hamiltonian flows Langevin Monte Carlo Runge-Kutta-Nyström Lipschitz conditions

2023-10-11abs ↗pdf ↗

Non-Canonical Hamiltonian Monte Carlostat.ML

New Hamiltonian Monte Carlo method for non-canonical dynamics.

problem Incompatibility of canonical symplectic structure with non-canonical dynamics.

method Developed a framework for Hamiltonian Monte Carlo using non-canonical symplectic structures with implicit integration.

result Non-canonical Hamiltonian Monte Carlo provides sampling advantages.

Hamiltonian Monte Carlo Non-canonical dynamics Symplectic geometry Implicit integration Sampling properties

2020-08-18abs ↗pdf ↗

Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networksstat.ML

New method combines neural networks with Monte Carlo for complex system reliability.

problem Estimating small failure probabilities in complex systems.

method Subset Simulation with Hamiltonian Neural Networks.

result High acceptance rates and computational efficiency in low-probability regions.

Subset Simulation Hamiltonian Monte Carlo Neural Networks Reliability Analysis Monte Carlo

2024-01-10abs ↗pdf ↗

Counterdiabatic Hamiltonian Monte Carlostat.ML

CHMC improves HMC efficiency for multimodal distributions.

problem Slow convergence of HMC in multimodal distributions.

method Integrates a counterdiabatic term to optimize Hamiltonian changes.

result CHMC achieves efficient sampling from challenging distributions.

Hamiltonian Monte Carlo Counterdiabatic term Multimodal distributions Sequential Monte Carlo Efficient sampling

2026-02-24abs ↗pdf ↗

Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inferencecs.LG

Gradient-based Monte Carlo sampling algorithms, like Langevin dynamics and Hamiltonian Monte Carlo, are important methods for Bayesian inference. In large-scale settings, full-gradients are not affordable and thus stochastic gradients evaluated on mini-batches are used as a replacement. In order to reduce the high vari…

2018-03-29abs ↗pdf ↗

Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densitiesstat.CO

New method samples from piecewise smooth distributions using Hamiltonian Monte Carlo.

problem Sampling from distributions with discontinuous gradients.

method Generalized Randomized Hamiltonian Monte Carlo (GRHMC) for piecewise smooth targets.

result GRHMC processes sample from piecewise smooth target distributions with the desired distribution as the invariant distribution.

Hamiltonian Monte Carlo piecewise smooth discontinuous gradients invariant distribution spike and slab prior

2025-04-25abs ↗pdf ↗

Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Ratesstat.CO

New algorithm speeds up MCMC for complex distributions.

problem Efficient sampling from complex, high-dimensional distributions.

method Numerical Generalized Randomized Hamiltonian Monte Carlo with state-dependent event rates.

result Approximates Hamiltonian trajectories for robust sampling.

Monte Carlo methods Hamiltonian Monte Carlo piecewise deterministic Markov processes adaptive numerical integration state-dependent event rates

2020-05-04abs ↗pdf ↗

Discussion of "Riemann manifold Langevin and Hamiltonian Monte Carlo methods'' by M. Girolami and B. Calderheadstat.CO

This technical report is the union of two contributions to the discussion of the Read Paper "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by B. Calderhead and M. Girolami, presented in front of the Royal Statistical Society on October 13th 2010 and to appear in the Journal of the Royal Statistical Soc…

2010-10-30abs ↗pdf ↗

Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlostat.ML

Researchers estimate optimal PAC-Bayes bounds using Hamiltonian Monte Carlo.

problem Estimating tight PAC-Bayes bounds with restricted posterior families.

method Sampling from optimal Gibbs posterior using Hamiltonian Monte Carlo, estimating KL divergence, and proposing high-probability bounds.

result Significant tightness gaps in PAC-Bayes bounds, up to 5-6% in some cases.

PAC-Bayes Hamiltonian Monte Carlo KL divergence Gibbs posterior MNIST

2023-10-30abs ↗pdf ↗

GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlostat.CO

GIST adapts HMC by tuning parameters based on position and momentum.

problem Locally adaptive sampling in Hamiltonian Monte Carlo.

method GIST uses Gibbs sampling to adaptively tune HMC parameters.

result GIST improves sampling efficiency for high-dimensional models.

Hamiltonian Monte Carlo Gibbs sampling locally adaptive path length Monte Carlo methods

2024-04-23abs ↗pdf ↗

Magnetic Hamiltonian Monte Carlostat.ML

Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we present a generalization of HMC which exploits \textit{non-canonical} Hamiltonian dynamics. We refer to this algorithm as magnetic HMC, since in 3 dimensions a subset of th…

2016-07-10abs ↗pdf ↗

Relativistic Monte Carlostat.ML

Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a Metropolis-Hastings algorithm by simulating the dynamics of a Hamiltonian system. However, HMC is sensitive to large time discretizations and performs poorly if there is a mismatch between the spatial geo…

2016-09-14abs ↗pdf ↗

Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficientstat.ML

Hamiltonian Monte Carlo on ReLU networks is inefficient due to large local error.

problem Inefficiency of Hamiltonian Monte Carlo on ReLU neural networks.

method Analysis of Hamiltonian Monte Carlo with leapfrog integrator for Bayesian neural network inference.

result Leapfrog HMC for ReLU networks has a large local error rate of

Ω(ε)

, leading to inefficiency.

Hamiltonian Monte Carlo ReLU networks Bayesian inference Efficiency Local error

2024-10-29abs ↗pdf ↗

Resonances in reflective Hamiltonian Monte Carlostat.ML

Reflective Hamiltonian Monte Carlo struggles with high-dimensional sampling.

problem Slow mixing in reflective Hamiltonian Monte Carlo with inexact reflections.

method Quantifying instantaneous non-uniformity with Sinkhorn divergence; analyzing particle motion in spheres and cubes; constructing low-dimensional toy models.

result Particles spontaneously unmix, leading to resonances in particle density.

Hamiltonian Monte Carlo reflective Sinkhorn divergence mixing problems particle density

2025-04-16abs ↗pdf ↗

Convergence of Preconditioned Hamiltonian Monte Carlo on Hilbert Spacesmath.PR

pHMC converges on infinite-dimensional spaces with bounds.

problem Convergence of pHMC on Hilbert spaces.

method Coupling of two pHMC copies, adapted from arXiv:1805.00452.

result Proven convergence bounds in 1-Wasserstein distance.

Hamiltonian Monte Carlo infinite-dimensional spaces Hilbert spaces convergence bounds Wasserstein distance

2020-11-17abs ↗pdf ↗

On the Dissipation of Ideal Hamiltonian Monte Carlo Samplerstat.CO

Improved efficiency in HMC samplers reduces dissipative behavior.

problem Reducing dissipative behavior in HMC samplers.

method Variable integration time and partial velocity refreshment.

result Efficiency improved by a √κ factor in Wasserstein-2 distance.

Hamiltonian Monte Carlo dissipation integration time velocity refreshment Wasserstein-2 distance

2022-09-15abs ↗pdf ↗

Sampling from high-dimensional, multimodal distributions using automatically tuned, tempered Hamiltonian Monte Carlostat.CO

New method improves sampling from complex, multi-peaked distributions.

problem Sampling from high-dimensional, multimodal distributions using HMC.

method Combines tempered HMC with automatic tuning strategies.

result Demonstrates more effective scaling with dimension than adaptive methods.

Hamiltonian Monte Carlo Tempering High-dimensional sampling Multimodality Automatic tuning

2021-11-12abs ↗pdf ↗

Delocalization of bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevinstat.CO

New method controls bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevin.

problem Bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevin samplers.

method Delocalization of bias technique applied to these samplers.

result Control

W_2

bias with

O(\sqrt{K})

integration steps for high-dimensional distributions.

bias Hamiltonian Monte Carlo underdamped Langevin delocalization integration steps

2026-07-16abs ↗pdf ↗

On Accelerated Mixing of the No-U-turn Samplermath.ST

New study on No-U-Turn Sampler for accelerated mixing in Hamiltonian Monte Carlo.

problem Achieving accelerated convergence in Hamiltonian Monte Carlo.

method Combining concentration of measure and coupling analysis for mixing.

result Rigorous mixing guarantees for the No-U-Turn Sampler in certain Gaussian distributions.

No-U-Turn Sampler Hamiltonian Monte Carlo concentration of measure coupling analysis accelerated mixing

2025-07-17abs ↗pdf ↗

Stochastic automatic differentiation for Monte Carlo processeshep-lat

This paper extends AD techniques to Monte Carlo processes for efficient derivative calculation.

problem Obtaining derivatives of expectation values in Monte Carlo processes.

method Two approaches: reweighting and Hamiltonian extension of HMC.

result Hamiltonian approach as a change of variables simplifies variance reduction.

Monte Carlo methods Automatic Differentiation Taylor series Variance reduction Hybrid Monte Carlo

2023-07-28abs ↗pdf ↗

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlocs.LG

HH-VAEM improves imputation and acquisition of missing data using hierarchical models and Hamiltonian Monte Carlo.

problem Imputation and acquisition of missing heterogeneous data.

method Hierarchical VAE model with Hamiltonian Monte Carlo and automatic hyper-parameter tuning.

result HH-VAEM outperforms existing methods in imputation and supervised learning tasks.

VAE Hierarchical Models Hamiltonian Monte Carlo Missing Data Imputation

2022-02-09abs ↗pdf ↗

Magnetic Manifold Hamiltonian Monte Carlostat.ML

Magnetic manifold HMC improves sampling on constrained manifolds.

problem Sampling from distributions restricted to embedded manifolds.

method Introduces magnetic manifold HMC, a generalization of HMC for constrained manifolds.

result Magnetic manifold HMC outperforms canonical manifold-constrained HMC.

Hamiltonian Monte Carlo Manifold Sampling Magnetic Field Symplectic Integrator Embedded Manifolds

2020-10-15abs ↗pdf ↗

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Familiesstat.ML

We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities where classical HMC is not an option due to intractable gradients, KMC adaptively learns the target's gradient structure by fitting an exponential family model in a Reprod…

2015-06-08abs ↗pdf ↗

Reflection coupling for unadjusted generalized Hamiltonian Monte Carlo in the nonconvex stochastic gradient casemath.PR

New method improves sampling efficiency in complex stochastic systems.

problem Sampling efficiency in nonconvex stochastic gradient cases.

method Reflection coupling for unadjusted generalized Hamiltonian Monte Carlo.

result Quantitative Gaussian concentration bounds and convergence rates established.

Monte Carlo methods Hamiltonian dynamics Wasserstein distance nonconvex optimization stochastic gradients

2023-10-28abs ↗pdf ↗

A Markov Jump Process for More Efficient Hamiltonian Monte Carlostat.ML

In most sampling algorithms, including Hamiltonian Monte Carlo, transition rates between states correspond to the probability of making a transition in a single time step, and are constrained to be less than or equal to 1. We derive a Hamiltonian Monte Carlo algorithm using a continuous time Markov jump process, and ar…

2015-09-13abs ↗pdf ↗

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theorycs.LG

FA-HMC improves Bayesian federated learning with rigorous guarantees.

problem Parameter estimation and uncertainty quantification in non-iid distributed data.

method Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC) with convergence guarantees.

result FA-HMC achieves better convergence and communication efficiency than existing methods.

Bayesian federated learning Hamiltonian Monte Carlo non-iid data convergence guarantees communication efficiency

2024-07-09abs ↗pdf ↗

Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splittingstat.ML

New method uses symmetric splitting for efficient HMC inference in large neural networks.

problem Efficient inference for Bayesian neural networks with large datasets.

method Introduces a symmetric integration scheme for Hamiltonian Monte Carlo (HMC) that does not rely on stochastic gradients.

result Symmetric splitting leads to more efficient HMC inference over large data sets.

Hamiltonian Monte Carlo Bayesian Neural Networks Large Data Symmetric Splitting Efficient Inference

2020-10-14abs ↗pdf ↗

Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlocs.LG

A new decentralized Bayesian learning method using Metropolis-adjusted Hamiltonian Monte Carlo.

problem Decentralized Bayesian learning with uncertainty quantification.

method Metropolis-adjusted Hamiltonian Monte Carlo in a decentralized federated learning setting.

result Theoretical guarantees and numerical effectiveness of the method on non-convex problems.

Bayesian learning decentralized learning Hamiltonian Monte Carlo federated learning Metropolis adjustment

2021-07-15abs ↗pdf ↗

Tamed Stochastic Gradient Hamiltonian Monte Carlomath.OC

A new tamed stochastic gradient Hamiltonian Monte Carlo algorithm for superlinearly growing stochastic gradients.

problem Sampling and stochastic optimization problems with superlinearly growing stochastic gradients.

method Tamed Stochastic Gradient Hamiltonian Monte Carlo (tSGHMC) algorithm.

result Established a non-asymptotic error bound in Wasserstein-2 distance with a convergence rate of

1/4

tamed stochastic gradient Hamiltonian Monte Carlo superlinearly growing gradients Wasserstein-2 distance stochastic optimization

2026-07-16abs ↗pdf ↗

elhmc: An R Package for Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihoodstat.OT

R package for Bayesian empirical likelihood sampling using HMC.

problem Sampling from non-convex Bayesian empirical likelihood posteriors.

method Hamiltonian Monte Carlo (HMC) algorithm for numerical integration.

result Efficient HMC sampling from BayesEL posteriors.

Bayesian empirical likelihood Hamiltonian Monte Carlo empirical likelihood non-convex posteriors MCMC

2022-09-02abs ↗pdf ↗

Nonparametric Hamiltonian Monte Carlocs.LG

NP-HMC extends HMC for nonparametric models in probabilistic programming.

problem Inference for nonparametric models in probabilistic programming.

method Introduces NP-HMC, a generalization of HMC for nonparametric models using tree representable functions.

result Empirically shows significant performance improvements over existing approaches.

Hamiltonian Monte Carlo nonparametric models probabilistic programming inference engines tree representable functions

2021-06-18abs ↗pdf ↗

Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlocs.LG

Estimating predictive uncertainty is crucial for many computer vision tasks, from image classification to autonomous driving systems. Hamiltonian Monte Carlo (HMC) is an sampling method for performing Bayesian inference. On the other hand, Dropout regularization has been proposed as an approximate model averaging techn…

2018-05-12abs ↗pdf ↗

Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process modelsstat.ML

Improves sampling efficiency for complex Bayesian models.

problem Inference challenges in hierarchical Gaussian-process models.

method Optimised Riemannian-manifold Hamiltonian Monte Carlo (RMHMC) with dynamic programming.

result Significant improvement in sampling efficiency and model evidence calculation.

Monte Carlo methods Gaussian processes Hamiltonian Monte Carlo Hierarchical models Bayesian inference

2025-11-09abs ↗pdf ↗

Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Modelscs.LG

We develop an HMC algorithm to easily marginalize random effects in LMMs.

problem Bayesian inference in LMMs is challenging, especially marginalizing random effects.

method Developed an HMC algorithm to marginalize random effects in LMMs efficiently.

result Marginalization is always beneficial when applicable and improves various models, especially cognitive science models.

Hamiltonian Monte Carlo Linear Mixed-Effects Models Marginalization Bayesian Inference Cognitive Sciences

2024-10-31abs ↗pdf ↗

Repelling-Attracting Hamiltonian Monte Carlomath.ST

RAHMC improves sampling from multimodal distributions using dissipative dynamics.

problem Sampling from multimodal distributions efficiently.

method RAHMC uses mode-repelling and mode-attracting stages with a single tuning parameter.

result RAHMC generates proposals that cross low-probability barriers efficiently.

Hamiltonian Monte Carlo multimodal distributions dissipative dynamics mode-repelling mode-attracting

2024-03-07abs ↗pdf ↗

Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learningstat.ML

Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) is a momentum version of stochastic gradient descent with properly injected Gaussian noise to find a global minimum. In this paper, non-asymptotic convergence analysis of SGHMC is given in the context of non-convex optimization, where subsampling techniques are used o…

2019-03-25abs ↗pdf ↗

An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Modelsstat.ML

This technical report presents pseudo-code for a Riemannian manifold Hamiltonian Monte Carlo (RMHMC) method to efficiently simulate samples from $N$ -dimensional posterior distributions $p(x|y)$ , where $x \in R^N$ is drawn from a Gaussian Process (GP) prior, and observations $y_n$ are independent given $x_n$ . Sufficient…

2018-10-28abs ↗pdf ↗

Generalizing Hamiltonian Monte Carlo with Neural Networksstat.ML

We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is trained to maximize expected squared jumped distance, a proxy for mixing speed. We demon…

2017-11-25abs ↗pdf ↗

Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random stepsstat.ML

HMC improves Gaussian sampling efficiency with long, random steps.

problem Efficiently sampling from high-dimensional Gaussian distributions.

method Hamiltonian Monte Carlo with long and random integration times.

result HMC achieves

\varepsilon

-closeness in total variation distance with

\widetilde{O}(\sqrt{\kappa} d^{1/4} \log(1/\varepsilon))

gradient queries.

Hamiltonian Monte Carlo Gaussian sampling Markov chain Total variation distance Gradient queries

2022-09-26abs ↗pdf ↗

A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare Datacs.LG

A new method uses Hamiltonian Monte Carlo for imputation and augmentation of healthcare data.

problem Missing values in clinical studies lead to biased results and loss of statistical power.

method Folded Hamiltonian Monte Carlo (F-HMC) with Bayesian inference to handle high-dimensional, small sample size datasets.

result The method enriches the quality of data in precision, accuracy, recall, F1 score, and propensity metric.

Hamiltonian Monte Carlo Bayesian inference healthcare data imputation augmentation

2021-03-03abs ↗pdf ↗

Faster MCMC for Gaussian Latent Position Network Modelsstat.CO

New MCMC methods improve efficiency for large network inference.

problem Efficiency of Metropolis within Gibbs for large networks.

method Combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo.

result New methods outperform Metropolis within Gibbs on synthetic and real networks.

MCMC Hamiltonian Monte Carlo Firefly Monte Carlo Bayesian inference Latent position networks

2020-06-13abs ↗pdf ↗

Towards Unifying Hamiltonian Monte Carlo and Slice Samplingstat.ML

We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics. This insight enables extension of HMC and slice sampling to a broader family of samplers, called Monomial Gamma Samplers (MGS). We provide a theoretical anal…

2016-02-25abs ↗pdf ↗

Coupling and Convergence for Hamiltonian Monte Carlomath.PR

Based on a new coupling approach, we prove that the transition step of the Hamiltonian Monte Carlo algorithm is contractive w.r.t. a carefully designed Kantorovich (L1 Wasserstein) distance. The lower bound for the contraction rate is explicit. Global convexity of the potential is not required, and thus multimodal targ…

2018-05-01abs ↗pdf ↗

Gaussian Process Regression with Soft Inequality and Monotonicity Constraintsstat.ML

A new GP method enforces physical constraints in probabilistic terms.

problem Unbounded model in GP regression leading to infeasible values.

method Introduces a new GP method using QHMC to enforce soft inequality and monotonicity constraints.

result Improves accuracy and reduces variance in GP model.

Gaussian process Monte Carlo Hamiltonian Monte Carlo Inequality constraints Monotonicity constraints

2024-04-03abs ↗pdf ↗