mlsanityData to 7 Oct 2026

Streaming Variational Bayes

arXiv 1307.6769 ↗First posted 25 Jul 2013stat.ML cs.LGcited by 46

Not on the map. Why?

Abstract (arXiv)

We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)synchronous computation of a Bayesian posterior. The framework makes streaming updates to the estimated posterior according to a user-specified approximation batch primitive. We demonstrate the usefulness of our framework, with variational Bayes (VB) as the primitive, by fitting the latent Dirichlet allocation model to two large-scale document collections. We demonstrate the advantages of our algorithm over stochastic variational inference (SVI) by comparing the two after a single pass through a known amount of data---a case where SVI may be applied---and in the streaming setting, where SVI does not apply.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2013.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 5; 2023: 9; 2024: 10; 2025: 6; 2026: 6.

46 papers.

Sort:

By area:

  1. 2021Online Coreset Selection for Rehearsal-based Continual Learningcited by 114
  2. 2021Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstructioncited by 39
  3. 2023Continual Learning via Sequential Function-Space Variational Inferencecited by 28
  4. 2021Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptationcited by 24
  5. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  6. 2023Low-rank extended Kalman filtering for online learning of neural networks from streaming datacited by 23
  7. 2025Model Predictive Task Sampling for Efficient and Robust Adaptationcited by 20
  8. 2021Generalizing to the Open World: Deep Visual Odometry with Online Adaptationcited by 19
  9. 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approachcited by 17
  10. 2024Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Resetcited by 17
  11. 2022Partitioned Variational Inference: A Framework for Probabilistic Federated Learningcited by 13
  12. 2025Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environmentscited by 11
34 more
  1. 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisitioncited by 8
  2. 2021Knowing when we do not know: Bayesian continual learning for sensing-based analysis taskscited by 7
  3. 2023Particle-based Online Bayesian Samplingcited by 7
  4. 2024A Comprehensive Guide to Explainable AI: From Classical Models to LLMscited by 7
  5. 2023Streaming Factor Trajectory Learning for Temporal Tensor Decompositioncited by 6
  6. 2025Knowledge Adaptation as Posterior Correctioncited by 6
  7. 2023Machine Learning and the Future of Bayesian Computationcited by 4
  8. 2024Exploring the Stability Gap in Continual Learning: The Role of the Classification Headcited by 4
  9. 2024Streaming Bayes GFlowNetscited by 4
  10. 2021Online Meta Adaptation for Variable-Rate Learned Image Compressioncited by 3
  11. 2023Streaming LifeLong Learning With Any-Time Inferencecited by 3
  12. 2024Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue Systemcited by 3
  13. 2024Embarrassingly Parallel GFlowNetscited by 3
  14. 2021Non-negative matrix factorization algorithms generally improve topic model fitscited by 2
  15. 2022BAM: Bayes with Adaptive Memorycited by 2
  16. 2022Differentially private partitioned variational inferencecited by 2
  17. 2024Lifelong Learning of Video Diffusion Models From a Single Video Streamcited by 2
  18. 2026Soft Bayesian Context Tree Models for Real-Valued Time Seriescited by 2
  19. 2021Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learningcited by 1
  20. 2022Streaming Inference for Infinite Non-Stationary Clusteringcited by 1
  21. 2023Towards Robust Continual Learning with Bayesian Adaptive Moment Regularizationcited by 1
  22. 2025Unlearning in Diffusion models under Data Constraints: A Variational Inference Approachcited by 1
  23. 2026Efficient Online Variational Estimation via Monte Carlo Samplingcited by 1
  24. 2026Adaptive Bayesian Online Learning via Expert Aggregationcited by 1
  25. 2021Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inferencecited by 0
  26. 2022Dirichlet process mixture models for non-stationary data streamscited by 0
  27. 2023Streaming data recovery via Bayesian tensor train decompositioncited by 0
  28. 2024Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observationscited by 0
  29. 2024Representation and De-interleaving of Mixtures of Hidden Markov Processescited by 0
  30. 2024Fisher Information based Stochastic Gradient Ascent for Online Learning of Dirichlet Process Mixture and Theorycited by 0
  31. 2025Predictive Coresetscited by 0
  32. 2026From Gradients to Riccati Geometry: Kalman World Models for Single-Pass Learningcited by 0
  33. 2026Active Continual Learning with Metaplastic Binary Bayesian Neural Networkscited by 0
  34. 2026Fast rates in Bayesian online learning with approximate posteriorscited by 0