mlsanityData to 7 Oct 2026

Particle-based Online Bayesian Sampling

arXiv 2302.14796 ↗First posted 28 Feb 2023cs.LGcited by 7

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Abstract (arXiv)

Online optimization has gained increasing interest due to its capability of tracking real-world streaming data. Although online optimization methods have been widely studied in the setting of frequentist statistics, few works have considered online optimization with the Bayesian sampling problem. In this paper, we study an Online Particle-based Variational Inference (OPVI) algorithm that uses a set of particles to represent the approximating distribution. To reduce the gradient error caused by the use of stochastic approximation, we include a sublinear increasing batch-size method to reduce the variance. To track the performance of the OPVI algorithm with respect to a sequence of dynamically changing target posterior, we provide a detailed theoretical analysis from the perspective of Wasserstein gradient flow with a dynamic regret. Synthetic and Bayesian Neural Network experiments show that the proposed algorithm achieves better results than naively applying existing Bayesian sampling methods in the online setting.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 27 of its 57 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

27 of 57 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 498
  3. 2017Don't Decay the Learning Rate, Increase the Batch Sizecited by 313
  4. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
  5. 2018Bayesian Model-Agnostic Meta-Learningcited by 194
  6. 2013Optimization, Learning, and Games with Predictable Sequencescited by 187
  7. 2018AdaGrad stepsizes: Sharp convergence over nonconvex landscapescited by 182
  8. 2013Non-stationary Stochastic Optimizationcited by 173
  9. 2017Online Convex Optimization with Stochastic Constraintscited by 94
  10. 2016First-order Methods for Geodesically Convex Optimizationcited by 92
  11. 2018Bayesian graph convolutional neural networks for semi-supervised classificationcited by 88
  12. 2016Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradientcited by 65
15 more
  1. 2016Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifoldscited by 64
  2. 2020A Non-Asymptotic Analysis for Stein Variational Gradient Descentcited by 61
  3. 2013Streaming Variational Bayescited by 46
  4. 2018A Unified Particle-Optimization Framework for Scalable Bayesian Samplingcited by 44
  5. 2018On the Fenchel Duality between Strong Convexity and Lipschitz Continuous Gradientcited by 42
  6. 2017Dropout Inference in Bayesian Neural Networks with Alpha-divergencescited by 28
  7. 2019A Generalization Bound for Online Variational Inferencecited by 17
  8. 2014Stochastic Variational Inference for Hidden Markov Modelscited by 12
  9. 2018Variance Reduction in Stochastic Particle-Optimization Samplingcited by 11
  10. 2021A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learningcited by 11
  11. 2021Kalman Bayesian Neural Networks for Closed-form Online Learningcited by 9
  12. 2022A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learningcited by 9
  13. 2020Probabilistic Programs with Stochastic Conditioningcited by 8
  14. 2017Tracking Moving Agents via Inexact Online Gradient Descent Algorithmcited by 6
  15. 2019Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Samplingcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 0; 2026: 1.

7 papers.

  1. 2023Low-rank extended Kalman filtering for online learning of neural networks from streaming datacited by 23
  2. 2024Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Datacited by 5
  3. 2023On-ramp and Off-ramp Traffic Flows Estimation Based on A Data-driven Transfer Learning Frameworkcited by 3
  4. 2023A Transfer Learning Framework for Proactive Ramp Metering Performance Assessmentcited by 3
  5. 2024Electrostatics-based particle sampling and approximate inferencecited by 2
  6. 2023A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labelscited by 1
  7. 2026Fast rates in Bayesian online learning with approximate posteriorscited by 0