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Distribution Matching for Machine Teaching.

Xiaofeng Cao, Ivor W Tsang

    IEEE Transactions on Neural Networks and Learning Systems
    |April 8, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Machine teaching, an inverse of machine learning, now offers a new strategy using distribution matching. This approach finds optimal teaching examples efficiently, even without knowing student learning parameters, providing a closed-form solution for training examples.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Science

    Background:

    • Machine teaching is the inverse of machine learning, guiding a student model to a target hypothesis.
    • Existing methods struggle when student learning parameters are unknown.
    • Previous approaches focused on balancing teaching risk and cost using the student model.

    Purpose of the Study:

    • To develop a novel machine teaching strategy for scenarios where student learning parameters are undisclosed.
    • To present a distribution matching-based approach to optimize teaching example selection.
    • To enable effective machine teaching under limited teaching cost constraints.

    Main Methods:

    • Introduced a distribution matching-based machine teaching strategy.
    • Utilized iterative shrinking of teaching cost in a smooth surrogate to avoid boundary perturbations.
    • Defined the strategy as a cost-controlled optimization process.
    • Derived a closed-form expression for training examples given a limited teaching cost.

    Main Results:

    • The proposed strategy effectively finds optimal teaching examples without exploring student parameter distribution.
    • Eliminates boundary perturbations from the version space through a smooth surrogate.
    • Demonstrated effectiveness through theoretical analysis and experimental results.
    • Provides a closed-form solution for training examples under cost constraints.

    Conclusions:

    • The distribution matching-based machine teaching strategy is effective for unknown student parameters.
    • The method offers an efficient way to select optimal teaching examples.
    • This approach provides a closed-form solution for training examples, enhancing machine teaching capabilities.