arXiv research

The paper proves almost sure convergence of MCES algorithm for a specific class of MDPs.

Along with the recent advances in scalable Markov Chain Monte Carlo methods, sampling techniques that are based on Langevin diffusions have started receiving increasing attention. These so called Langevin Monte Carlo (LMC) methods are based on diffusions driven by a Brownian motion, which gives rise to Gaussian proposa…

Paper proposes an ensemble-based AIS for multimodal sampling.

VCSMC improves efficiency in Bayesian phylogenetic inference.

New algorithm speeds up HMC by generating a warm start in O(d^1/4) iterations.

Enhances SMC² with Hessian info for more efficient posterior approximation.

The paper improves SMC algorithm for multi-modal distributions by proving variance bounds.

This work improves convergence guarantees for unadjusted HMC in KL and Rényi divergences.

Develops a neural surrogate for proton dose calculation using Monte Carlo dropout uncertainty.

The paper uses Fourier integral theorem for estimating multivariate distributions.

The paper uses LSM to solve complex monetary utility functions.

Adaptive SAA solves large-scale stochastic linear programs efficiently.

Developed scalable Monte Carlo method for VIX option pricing.

S-VBMC improves VBMC's exploration of complex posterior distributions.

The paper introduces new estimators for multivariate functions using Fourier methods.

We introduce a gradient-based learning method to automatically adapt Markov chain Monte Carlo (MCMC) proposal distributions to intractable targets. We define a maximum entropy regularised objective function, referred to as generalised speed measure, which can be robustly optimised over the parameters of the proposal di…

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

A Monte Carlo method for pairs trading on mean-reverting spreads with Lévy processes.

MCNO learns PDE solution operators using Monte Carlo sampling.

This thesis tackles non-convex Bayesian learning via scalable dynamic importance sampling algorithms.

ChEES-HMC improves SMC samplers' efficiency and speed.

This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a function with respect to parameters defining the distribution that is integrated; t…

Unbiased method for Bayesian posterior means using kinetic Langevin dynamics.

Paper proposes MCTSPO for better reinforcement learning policy optimization.

Study improves sampling from complex distributions using annealed Langevin Monte Carlo.

We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a vector of random numbers u, in such a way that the outcome, knowing u, is determinis…

Improved efficiency in HMC samplers reduces dissipative behavior.

Active Reinforcement Learning (ARL) is a twist on RL where the agent observes reward information only if it pays a cost. This subtle change makes exploration substantially more challenging. Powerful principles in RL like optimism, Thompson sampling, and random exploration do not help with ARL. We relate ARL in tabular …

Combines MCTS and neural networks for efficient multi-period financial planning.

Simple mode exploration methods do not improve performance in neural networks.

This paper improves Bayesian decision tree learning using HMC.

The paper explores how control variates can reduce variance in Monte Carlo simulations, especially for Sobolev functions.

Computing the marginal likelihood (ML) of a model requires marginalizing out all of the parameters and latent variables, a difficult high-dimensional summation or integration problem. To make matters worse, it is often hard to measure the accuracy of one's ML estimates. We present bidirectional Monte Carlo, a technique…

Markov Chain Monte Carlo methods have revolutionised mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamiltonian dynamics have been proposed as an efficient way of building chains which can explore probability densities efficiently. The method emerg…

This survey explores various optimality concepts in importance sampling.

We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampling for problems where we can leverage (stochastic) gradients to define continuous dynamics which explore the target distribution. We outline a solution strategy for this setting based on stochastic gradient Hamiltonian Monte Carlo sampling (SGHMC) …

The AutoML task consists of selecting the proper algorithm in a machine learning portfolio, and its hyperparameter values, in order to deliver the best performance on the dataset at hand. Mosaic, a Monte-Carlo tree search (MCTS) based approach, is presented to handle the AutoML hybrid structural and parametric expensiv…

This paper explores alternative regression techniques in pricing American put options and compares to the least-squares method (LSM) in Monte Carlo implemented by Longstaff-Schwartz, 2001 which uses least squares to estimate the conditional expected payoff to the option holder from continuation. The pricing is done und…

Paper addresses theoretical risks in neural MCCFR, proposing Robust Deep MCCFR for improved performance.

A new metric tensor improves Riemann manifold Monte Carlo for Bayesian models.

Quantum RNG improves financial risk metrics estimation.

In this paper, we present an online reinforcement learning algorithm, called Renewal Monte Carlo (RMC), for infinite horizon Markov decision processes with a designated start state. RMC is a Monte Carlo algorithm and retains the advantages of Monte Carlo methods including low bias, simplicity, and ease of implementatio…

New method tunes SMC samplers efficiently without high costs.

Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when the target posterior is multimodal, mixing performance is often poor. This results in inadequate exploration of the posterior distribution. A…

Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC). In recent years, however, several methods have been proposed based on combining variational Bayesian inference and MCMC simulation in order to improve their ov…

This work introduces efficient sampling methods for Gaussian processes by focusing on pathwise conditioning.

Paper proposes a new method for sampling from complex distributions.

LMC achieves sqrt(d) dependence in sampling error, improving previous bounds.