Tag: carlo
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Counterdiabatic Hamiltonian Monte Carlo
Counterdiabatic Hamiltonian Monte Carlo arXiv:2602.21272v1 Announce Type: new Abstract: Hamiltonian Monte Carlo (HMC) is a state of the art method for sampling from distributions with differentiable densities, but can converge slowly when applied to challenging multimodal problems. Running HMC with a time varying Hamiltonian, in order to interpolate from an initial tractable distribution to the…
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Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process models
Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process models arXiv:2511.06407v1 Announce Type: new Abstract: Hierarchical Bayesian models based on Gaussian processes are considered useful for describing complex nonlinear statistical dependencies among variables in real-world data. However, effective Monte Carlo algorithms for inference with these models have not yet been established, except for several simple cases.…
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Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles arXiv:2505.13585v1 Announce Type: new Abstract: This work introduces a new method called scalable Bayesian Monte Carlo (SBMC). The model interpolates between a point estimator and the posterior, and the algorithm is a parallel implementation of a consistent (asymptotically unbiased) Bayesian deep learning algorithm: sequential Monte…
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Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles arXiv:2505.11671v1 Announce Type: new Abstract: Sequential Monte Carlo (SMC) methods offer a principled approach to Bayesian uncertainty quantification but are traditionally limited by the need for full-batch gradient evaluations. We introduce a scalable variant by incorporating Stochastic Gradient Hamiltonian Monte Carlo (SGHMC)…