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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Exploring the Trade-Off in the Variational Information Bottleneck for Regression with a Single Training Run.

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Summary

This study introduces an efficient framework for Variational Information Bottleneck (VIB) in regression. It enables finding optimal solutions for all trade-off parameters (β) in a single training run, enhancing efficiency and understanding.

Keywords:
deep learninginformation bottleneckregression modelsupervised learning

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Area of Science:

  • Machine Learning
  • Deep Learning
  • Information Theory

Background:

  • Information Bottleneck (IB) theory provides a framework for learning compressed data representations.
  • Variational Information Bottleneck (VIB) is a standard deep learning implementation of IB.
  • The Lagrange multiplier β in VIB controls the trade-off between information retention and compression.

Purpose of the Study:

  • To analyze the optimal solution of VIB in regression problems.
  • To propose an efficient framework for VIB optimization in regression.
  • To explore the behavior and effects of IB in regression settings.

Main Methods:

  • Theoretical analysis of VIB in regression.
  • Development of a novel framework for VIB optimization.
  • Experimental validation of the proposed framework.

Main Results:

  • Derivation of the optimal solution for VIB in regression.
  • A single training run can yield optimal VIB solutions for all β values.
  • Demonstrated enhanced efficiency compared to conventional methods.

Conclusions:

  • The proposed framework significantly improves the efficiency of exploring VIB solutions in regression.
  • This work deepens the theoretical understanding of IB in regression tasks.
  • The approach offers a more streamlined method for VIB hyperparameter tuning.