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Learning Time-Varying Coverage Functions.

Nan Du1, Yingyu Liang2, Maria-Florina Balcan3

  • 1College of Computing, Georgia Institute of Technology.

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Summary
This summary is machine-generated.

This study introduces a new method for learning time-varying coverage functions, crucial for understanding social networks and machine learning. The novel approach improves influence estimation in information diffusion with enhanced accuracy.

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Area of Science:

  • Machine Learning
  • Social Network Analysis
  • Algorithmic Game Theory

Background:

  • Coverage functions model diminishing returns in various applications.
  • Existing methods lack efficient learning for dynamic coverage functions.

Purpose of the Study:

  • To introduce and address the problem of learning time-varying coverage functions.
  • To develop a novel parametrization and efficient learning algorithm for these functions.

Main Methods:

  • Parametrization of time-varying coverage functions using random features.
  • Development of an efficient parameter learning algorithm based on likelihood maximization.
  • Sample complexity analysis of the proposed algorithm.

Main Results:

  • The algorithm accurately estimates influence in information diffusion.
  • Demonstrated superior performance over existing approaches on synthetic and real-world data.
  • Effective even with minimal assumptions about diffusion processes.

Conclusions:

  • The proposed method offers an efficient and accurate way to learn time-varying coverage functions.
  • This advancement has significant implications for influence estimation in social network analysis.