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.

168,742 papers · 148 categories

Trend · papers per month

count % of corpus

48 results for “discontinuous Hamiltonian Monte Carlo”

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 ↗

Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networksmath.OC

Paper analyzes SGHMC for non-convex optimization with discontinuous gradients.

problem Training neural networks with ReLU activation.

method Non-asymptotic convergence analysis of SGHMC with discontinuous gradients.

result Explicit upper bounds for expected excess risk in non-convex optimization.

SGHMC non-asymptotic analysis discontinuous gradients non-convex optimization ReLu neural networks

2024-09-25abs ↗pdf ↗

Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuitiesstat.CO

Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs). However, the fact that HMC uses derivative information causes complications when the target distribution is non-differentiable with respect…

2018-04-07abs ↗pdf ↗

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 ↗

Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filtersstat.ML

Improved particle filters for estimating model parameters using differentiable resampling.

problem Inability to differentiate sampling and resampling steps in particle filters.

method Extended reparameterisation trick to include stochastic input, enabling differentiation. Used p-MCMC and NUTS for parameter estimation.

result NUTS improves mixing of Markov chain and produces more accurate results in less time.

particle filters differentiable resampling p-MCMC NUTS Hamiltonian Monte Carlo

2021-11-02abs ↗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 ↗

Stein Variational Inference for Discrete Distributionscs.LG

A method for efficient approximate inference on discrete distributions.

problem Applying SVGD to discrete distributions.

method Transforming discrete distributions to piecewise continuous distributions for SVGD application.

result Outperforms traditional algorithms and ensemble methods on discrete graphical models.

Stein variational gradient descent discrete distributions approximate inference goodness-of-fit test discontinuous Hamiltonian Monte Carlo

2020-03-01abs ↗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 ↗

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 ↗

Stochastic Algorithmic Differentiation of (Expectations of) Discontinuous Functions (Indicator Functions)q-fin.CP

In this paper, we present a method for the accurate estimation of the derivative (aka.~sensitivity) of expectations of functions involving an indicator function by combining a stochastic algorithmic differentiation and a regression. The method is an improvement of the approach presented in [Risk Magazine April 2018]. T…

2018-11-14abs ↗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 ↗

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Modelscs.LG

We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme is in its ability to automatically distinguish parameters the density functio…

2019-03-06abs ↗pdf ↗

Adaptive Multilevel Monte Carlo for Probabilitiesq-fin.CP

Adaptive Multilevel Monte Carlo improves probability estimation for complex random variables.

problem Estimating probabilities of complex random variables with multiple approximations.

method Adaptive Multilevel Monte Carlo framework for discontinuous functionals.

result Achieves optimal computational complexities for both smooth and discontinuous functionals.

Monte Carlo methods adaptive algorithms probability estimation discontinuous functionals nested simulation

2021-07-19abs ↗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 ↗

Discovering Heterogeneous Treatment Effects in Regression Discontinuity Designsecon.EM

Algorithm uncovers treatment effect heterogeneity in educational RD designs.

problem Discovering sources of treatment effect heterogeneity in regression discontinuity designs.

method Causal supervised machine learning algorithm to build a 'regression discontinuity tree'.

result Algorithm uncovers various sources of heterogeneity in the impact of attending a better secondary school.

regression discontinuity design treatment effect heterogeneity machine learning education policy Monte Carlo simulations

2021-06-22abs ↗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 ↗

Accelerated Inference for Partially Observed Markov Processes using Automatic Differentiationstat.ME

New AD methods improve likelihood estimation for partially observed systems.

problem Estimating likelihood functions for partially observed nonlinear systems.

method Embedding AD particle filter methods in a theoretical framework, developing new algorithms for likelihood maximization.

result Mean squared error significantly lower than existing algorithms.

Automatic differentiation Particle filters Likelihood maximization Monte Carlo methods Stochastic dynamical systems

2024-07-03abs ↗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 ↗

Monte Carlo pathwise sensitivities for barrier optionsq-fin.CP

The Monte Carlo pathwise sensitivities approach is well established for smooth payoff functions. In this work, we present a new Monte Carlo algorithm that is able to calculate the pathwise sensitivities for discontinuous payoff functions. Our main tool is to combine the one-step survival idea of Glasserman and Staum wi…

2018-04-11abs ↗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 ↗