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Tag: monte

  • 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…

  • 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.…

  • 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…

  • 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)…

  • Preconditioned Subspace Langevin Monte Carlo

    Preconditioned Subspace Langevin Monte Carlo arXiv:2412.13928v1 Announce Type: new Abstract: We develop a new efficient method for high-dimensional sampling called Subspace Langevin Monte Carlo. The primary application of these methods is to efficiently implement Preconditioned Langevin Monte Carlo. To demonstrate the usefulness of this new method, we extend ideas from subspace descent methods in Euclidean…

  • Langevin Monte Carlo Beyond Lipschitz Gradient Continuity

    Langevin Monte Carlo Beyond Lipschitz Gradient Continuity arXiv:2412.09698v1 Announce Type: new Abstract: We present a significant advancement in the field of Langevin Monte Carlo (LMC) methods by introducing the Inexact Proximal Langevin Algorithm (IPLA). This novel algorithm broadens the scope of problems that LMC can effectively address while maintaining controlled computational costs. IPLA extends LMC’s…