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Decorrelation using optimal transport.

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We developed a new method using Convex Neural Optimal Transport Solvers (Cnots) for decorrelating feature spaces from protected attributes. This approach shows significant gains in high energy physics, especially for multiclass classification tasks.

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Area of Science:

  • High Energy Physics
  • Machine Learning
  • Fairness in AI

Background:

  • Decorrelating feature spaces from protected attributes is crucial for fairness and scientific integrity.
  • Current methods face challenges, especially in complex, multidimensional scenarios.

Purpose of the Study:

  • Introduce a novel decorrelation method using Convex Neural Optimal Transport Solvers (Cnots).
  • Evaluate the method's performance in jet classification within high energy physics.
  • Compare its effectiveness against state-of-the-art techniques.

Main Methods:

  • Utilized optimal transport theory for feature space decorrelation.
  • Applied Convex Neural Optimal Transport Solvers (Cnots) to continuous feature spaces.
  • Tested the method on binary and multiclass jet classification tasks.

Main Results:

  • Achieved decorrelation levels comparable to state-of-the-art in binary classification.
  • Demonstrated significantly superior performance in multiclass classification.
  • Showcased effective decorrelation of continuous feature spaces against protected attributes.

Conclusions:

  • Cnots offers a powerful new approach for decorrelating feature spaces.
  • The method shows substantial promise for improving fairness and robustness in AI applications.
  • Optimal transport-based decorrelation presents significant gains for multidimensional feature spaces.