mlsanityData to 7 Oct 2026

Variations on the Expectation due to Changes in the Probability Measure

arXiv 2502.02887 ↗First posted 5 Feb 2025cs.IT cs.LG math.ITcited by 3

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Abstract (arXiv)

In this paper, closed-form expressions are presented for the variation of the expectation of a given function due to changes in the probability measure used for the expectation. They unveil interesting connections with Gibbs probability measures, mutual information, and lautum information.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 43 references that are in the map. First posted on arXiv 5 Feb 2025.

Builds on

16 of 43 references.

References · 16

  1. 2015On the properties of variational approximations of Gibbs posteriorscited by 121
  2. 2019Distributionally Robust Optimization: A Reviewcited by 75
  3. 2023Generalization Bounds: Perspectives from Information Theory and PAC-Bayescited by 60
  4. 2020Optimal Bounds between f-Divergences and Integral Probability Metricscited by 27
  5. 2024What is the long-run distribution of stochastic gradient descent? A large deviations analysiscited by 17
  6. 2021Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithmcited by 14
  7. 2022Empirical Risk Minimization with Relative Entropy Regularizationcited by 13
  8. 2023Flow-based Distributionally Robust Optimizationcited by 13
  9. 2023On the Validation of Gibbs Algorithms: Training Datasets, Test Datasets and their Aggregationcited by 10
  10. 2021Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Modelcited by 9
  11. 2022Empirical Risk Minimization with Relative Entropy Regularization: Optimality and Sensitivity Analysiscited by 8
  12. 2023Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measurecited by 7
4 more
  1. 2022How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?cited by 5
  2. 2024The Method of Gaps: Exact Expressions for the Generalization Error of Supervised Learning Algorithmscited by 5
  3. 2025Proofs for Folklore Theorems on the Radon-Nikodym Derivativecited by 4
  4. 2024An Information-Theoretic Framework for Out-of-Distribution Generalization with Applications to Stochastic Gradient Langevin Dynamicscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithmscited by 2
  2. 2026Equivalence Between Nested Gibbs Measures and Log-Linear Combinations of Gibbs Measurescited by 0
  3. 2026Machine Unlearning for Gibbs Supervised Learning Algorithmscited by 0