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.
AutoStep MCMC adapts step size locally for better sampling efficiency.
Unified framework for MCMC and machine learning problems.
We propose a method to construct a proposal density for the Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of the GARCH model. The proposal density is constructed adaptively by using the data sampled by the MCMC metho d itself. It turns out that autocorrelations between the data generated …
Adaptive workflow combines fast amortized inference with MCMC for many datasets.
New MCMC method learns sparse preconditioner for high-dimensional problems.
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…
Adaptive stopping in MCMC using classifier-based dynamics
This work improves VAEs using MCMC methods for better variational bounds.
Optimal preconditioning improves Langevin sampling efficiency.
Neural network MCMC sampler maximizes proposal entropy for efficient sampling.
It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs). But no more MCMC dynamics is understood in this way. In this work, by developing novel conce…
Stochastic gradient Markov chain Monte Carlo (SG-MCMC) methods are Bayesian analogs to popular stochastic optimization methods; however, this connection is not well studied. We explore this relationship by applying simulated annealing to an SGMCMC algorithm. Furthermore, we extend recent SG-MCMC methods with two key co…
New method reduces Gibbs partition function estimation complexity.
New MCMC method improves sampling efficiency across diverse structural models.
The modern scale of data has brought new challenges to Bayesian inference. In particular, conventional MCMC algorithms are computationally very expensive for large data sets. A promising approach to solve this problem is embarrassingly parallel MCMC (EP-MCMC), which first partitions the data into multiple subsets and r…
It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers. For the Poisson gamma belief network (PGBN), a recently …
Improved MCMC sampling for expensive, irregular likelihoods.
Adapts flow matching for MCMC to improve sampling efficiency.
APINNs use neural networks to solve MCMC problems efficiently.
This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to non-differentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex s…
New algorithm optimizes MCMC sampling for structural dynamic models.
LSB is a new MCMC method for discrete spaces that reduces target evaluations.
A new method reduces complexity of normalizing flows for MCMC preconditioning.
RLMH improves adaptive MCMC by optimizing contrastive divergence reward.
New MCMC algorithm reduces subset selection passes to 2 for optimal -dimensional subspace approximation.
Bayesian neural networks tutorial via MCMC in Python.
A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the purpose of sampling from a target distribution with strongly nonlinear support. The algorithm embeds the trajectory of the Markov chain into a reproducing kernel Hilbert space (RKHS), such that the feature space covariance of the samples informs the…
Markov chain Monte Carlo (MCMC) methods are widely used in machine learning. One of the major problems with MCMC is the question of how to design chains that mix fast over the whole state space; in particular, how to select the parameters of an MCMC algorithm. Here we take a different approach and, similarly to paralle…
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…
Develops a new MCMC-based Wishart prior for Gaussian Process covariance matrix.
Adaptive Bayesian model for covariate-dependent power spectra analysis.
Adaptive Monte Carlo schemes developed over the last years usually seek to ensure ergodicity of the sampling process in line with MCMC tradition. This poses constraints on what is possible in terms of adaptation. In the general case ergodicity can only be guaranteed if adaptation is diminished at a certain rate. Import…
New method speeds up Bayesian inverse problem solving with neural operators.
New method uses diffusion models to speed up MCMC sampling.
Adaptive-stepsize MCMC sampling inspired by Adam optimizer.
New algorithm speeds up MCMC for deep learning models.
In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky MCMC algorithms, for efficient sampling from a generic target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities which become closer and closer to …
Proposes a method to improve SLMC for multimodal distributions.
Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power. In particular, information theoretic bounded rationality models formalize resource constraints abstractly in terms of relative Shannon information, namely the Kullback-Leibler Divergence between the agents' pr…
New PDMP samplers improve BNN inference with accelerated computation.
ESS improves MCMC efficiency for correlated & multimodal distributions.
Bayesian learning for forests and trees improves graph detection and structure learning.
Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performan…
Due to the escalating growth of big data sets in recent years, new Bayesian Markov chain Monte Carlo (MCMC) parallel computing methods have been developed. These methods partition large data sets by observations into subsets. However, for Bayesian nested hierarchical models, typically only a few parameters are common f…
A new method reduces variance in SGMCMC by preferentially subsampling data.
Unified framework for MCMC algorithms simplifies their design and application.
Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous dynamics to define a transition kernel that efficiently explores a target distribution. In tandem, a focus has been on devising scalable variants that subsample the data and use stochastic gradients in place of full-data gradients in the dynamic s…
We propose a novel adaptive empirical Bayesian method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The proposed method works by alternatively sampling from an adaptive hierarchical posterior distribution using stochastic gradient Markov Chain Monte Carlo (M…