mlsanityData to 7 Oct 2026

Data Distribution Valuation Using Generalized Bayesian Inference

arXiv 2604.05993 ↗First posted 7 Apr 2026cs.LG stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

We investigate the data distribution valuation problem, which aims to quantify the values of data distributions from their samples. This is a recently proposed problem that is related to but different from classical data valuation and can be applied to various applications. For this problem, we develop a novel framework called Generalized Bayes Valuation that utilizes generalized Bayesian inference with a loss constructed from transferability measures. This framework allows us to solve, in a unified way, seemingly unrelated practical problems, such as annotator evaluation and data augmentation. Using the Bayesian principles, we further improve and enhance the applicability of our framework by extending it to the continuous data stream setting. Our experiment results confirm the effectiveness and efficiency of our framework in different real-world scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 45 references that are in the map. First posted on arXiv 7 Apr 2026.

Builds on

36 of 45 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  5. 2016Variational Inference: A Review for Statisticianscited by 1,466
  6. 2017Variational Continual Learningcited by 335
  7. 2021User-friendly introduction to PAC-Bayes boundscited by 234
  8. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 225
  9. 2020LEEP: A New Measure to Evaluate Transferability of Learned Representationscited by 182
  10. 2013A General Framework for Updating Belief Distributionscited by 172
  11. 2021LogME: Practical Assessment of Pre-trained Models for Transfer Learningcited by 168
  12. 2020Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Countscited by 163
24 more
  1. 2021Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learningcited by 162
  2. 2019Transferability and Hardness of Supervised Classification Taskscited by 125
  3. 2022An Information-Theoretic Approach to Transferability in Task Transfer Learningcited by 79
  4. 2022Variational Model Inversion Attackscited by 75
  5. 2023LAVA: Data Valuation without Pre-Specified Learning Algorithmscited by 69
  6. 2021OTCE: A Transferability Metric for Cross-Domain Cross-Task Representationscited by 62
  7. 2021Transferability Estimation using Bhattacharyya Class Separabilitycited by 53
  8. 2020Ranking Neural Checkpointscited by 50
  9. 2017Assigning a value to a power likelihood in a general Bayesian modelcited by 46
  10. 2018Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Datacited by 25
  11. 2021Transferability Metrics for Selecting Source Model Ensemblescited by 24
  12. 2022Fundamentals of Task-Agnostic Data Valuationcited by 19
  13. 2023ETran: Energy-Based Transferability Estimationcited by 18
  14. 2021Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performancecited by 15
  15. 2022Generalised Bayesian Inference for Discrete Intractable Likelihoodcited by 14
  16. 2024EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classificationcited by 13
  17. 2021Generalized Bayesian Likelihood-Free Inferencecited by 11
  18. 2019Focused Bayesian Predictioncited by 6
  19. 2023Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approachcited by 5
  20. 2023Simple Transferability Estimation for Regression Taskscited by 5
  21. 2024Data Distribution Valuationcited by 5
  22. 2025Sample-aware RandAugment: Search-free Automatic Data Augmentation for Effective Image Recognitioncited by 5
  23. 2022Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracycited by 4
  24. 2025Benchmarking Transferability: A Framework for Fair and Robust Evaluationcited by 2

Built on by

Not cited yet.