R-ParVI: Particle-based variational inference through lens of rewards

• URL: http://arxiv.org/abs/2502.20482v1
• Date: Thu, 27 Feb 2025 19:50:22 GMT
• Title: R-ParVI: Particle-based variational inference through lens of rewards
• Authors: Yongchao Huang,
• Abstract summary: A reward-guided, gradient-free ParVI method is proposed for sampling partially known densities.<n>R-ParVI offers fast, flexible, scalable and inference for a class of probabilistic models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A reward-guided, gradient-free ParVI method, \textit{R-ParVI}, is proposed for sampling partially known densities (e.g. up to a constant). R-ParVI formulates the sampling problem as particle flow driven by rewards: particles are drawn from a prior distribution, navigate through parameter space with movements determined by a reward mechanism blending assessments from the target density, with the steady state particle configuration approximating the target geometry. Particle-environment interactions are simulated by stochastic perturbations and the reward mechanism, which drive particles towards high density regions while maintaining diversity (e.g. preventing from collapsing into clusters). R-ParVI offers fast, flexible, scalable and stochastic sampling and inference for a class of probabilistic models such as those encountered in Bayesian inference and generative modelling.

Related papers

• Semi-Implicit Functional Gradient Flow for Efficient Sampling [30.32233517392456]
  We propose a functional gradient ParVI method that uses perturbed particles with Gaussian noise as the approximation family. We show that the corresponding functional gradient flow, which can be estimated via denoising score matching with neural networks, exhibits strong theoretical convergence guarantees. In addition, we present an adaptive version of our method that automatically selects the appropriate noise magnitude during sampling.
  arXiv  Detail & Related papers  (2024-10-23T15:00:30Z)
• Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers [49.97755400231656]
  We present the first performance guarantee with explicit dimensional dependencies for general score-mismatched diffusion samplers. We show that score mismatches result in an distributional bias between the target and sampling distributions, proportional to the accumulated mismatch between the target and training distributions. This result can be directly applied to zero-shot conditional samplers for any conditional model, irrespective of measurement noise.
  arXiv  Detail & Related papers  (2024-10-17T16:42:12Z)
• Variational Inference Using Material Point Method [0.0]
  MPM-ParVI is a gradient-based particle sampling method for variational inference. It offers deterministic sampling and inference for a class of probabilistic models.
  arXiv  Detail & Related papers  (2024-07-26T17:19:50Z)
• Variational Inference via Smoothed Particle Hydrodynamics [0.0]
  A new variational inference method is proposed, based on smoothed particle hydrodynamics. It offers fast, flexible, scalable and deterministic sampling and inference for a class of probabilistic models.
  arXiv  Detail & Related papers  (2024-07-12T11:38:41Z)
• Dynamical Measure Transport and Neural PDE Solvers for Sampling [77.38204731939273]
  We tackle the task of sampling from a probability density as transporting a tractable density function to the target. We employ physics-informed neural networks (PINNs) to approximate the respective partial differential equations (PDEs) solutions. PINNs allow for simulation- and discretization-free optimization and can be trained very efficiently.
  arXiv  Detail & Related papers  (2024-07-10T17:39:50Z)
• Electrostatics-based particle sampling and approximate inference [0.0]
  A new particle-based sampling and approximate inference method, based on electrostatics and Newton mechanics principles, is introduced. A discrete-time, discrete-space algorithmic design is provided for usage in more general inference problems.
  arXiv  Detail & Related papers  (2024-06-28T16:53:06Z)
• On the Trajectory Regularity of ODE-based Diffusion Sampling [79.17334230868693]
  Diffusion-based generative models use differential equations to establish a smooth connection between a complex data distribution and a tractable prior distribution. In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion models.
  arXiv  Detail & Related papers  (2024-05-18T15:59:41Z)
• Sampling in Unit Time with Kernel Fisher-Rao Flow [0.0]
  We introduce a new mean-field ODE and corresponding interacting particle systems (IPS) for sampling from an unnormalized target density. The IPS are gradient-free, available in closed form, and only require the ability to sample from a reference density and compute the (unnormalized) target-to-reference density ratio.
  arXiv  Detail & Related papers  (2024-01-08T13:43:56Z)
• A Geometric Perspective on Diffusion Models [57.27857591493788]
  We inspect the ODE-based sampling of a popular variance-exploding SDE. We establish a theoretical relationship between the optimal ODE-based sampling and the classic mean-shift (mode-seeking) algorithm.
  arXiv  Detail & Related papers  (2023-05-31T15:33:16Z)
• Sampling with Mollified Interaction Energy Descent [57.00583139477843]
  We present a new optimization-based method for sampling called mollified interaction energy descent (MIED) MIED minimizes a new class of energies on probability measures called mollified interaction energies (MIEs) We show experimentally that for unconstrained sampling problems our algorithm performs on par with existing particle-based algorithms like SVGD.
  arXiv  Detail & Related papers  (2022-10-24T16:54:18Z)
• Flow-based sampling in the lattice Schwinger model at criticality [54.48885403692739]
  Flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications. We provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.
  arXiv  Detail & Related papers  (2022-02-23T19:00:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.