Corpus ID: 202773824

Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates

@article{Negrea2019InformationTheoreticGB,
  title={Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates},
  author={Jeffrey Negrea and Mahdi Haghifam and G. Dziugaite and Ashish Khisti and D. Roy},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.02151}
}
  • Jeffrey Negrea, Mahdi Haghifam, +2 authors D. Roy
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
    • In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and recently extended by Bu, Zou, and Veeravalli (2019). Our main contributions are significantly improved mutual information bounds for Stochastic Gradient Langevin Dynamics via data-dependent estimates. Our approach is based on the variational characterization of mutual information and the use of data-dependent priors that forecast the mini-batch gradient based… CONTINUE READING
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    References

    SHOWING 1-10 OF 36 REFERENCES
    On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning
    • 8
    • PDF
    Information matrices and generalization
    • 11
    On Variational Bounds of Mutual Information
    • 118
    • PDF
    Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints
    • 46
    • Highly Influential
    • PDF
    Generalization Error Bounds for Noisy, Iterative Algorithms
    • 24
    • Highly Influential
    • PDF
    Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy
    • 39
    • PDF
    Train faster, generalize better: Stability of stochastic gradient descent
    • 496
    • PDF
    Tightening Mutual Information Based Bounds on Generalization Error
    • 24
    • Highly Influential
    Chaining Mutual Information and Tightening Generalization Bounds
    • 32
    • PDF
    Information-theoretic analysis of stability and bias of learning algorithms
    • 34
    • PDF