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Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation.

Mao Li1, Kaiqi Jiang1, Xinhua Zhang1

  • 1Department of Computer Science, University of Illinois at Chicago Chicago, IL 60607.

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|August 11, 2022
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Summary
This summary is machine-generated.

We introduce a novel bi-level optimization approach to improve probability discrepancy measures for machine learning tasks. This method warps measures towards end tasks, enhancing performance in unsupervised domain adaptation.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Statistical Learning Theory

Background:

  • Probability discrepancy measures are crucial for machine learning models like weakly supervised learning and generative modeling.
  • Existing measures often fail to account for distributions serving as input for downstream predictors, not as the final learning output.

Purpose of the Study:

  • To develop a probability discrepancy measure that is optimized for downstream prediction tasks.
  • To enhance the effectiveness of probability discrepancy measures by aligning them with the end goals of machine learning models.

Main Methods:

  • A novel bi-level optimization framework is proposed to warp probability discrepancy measures.
  • The approach compares distributions with respect to the optimal predictor for the downstream task, rather than uniformly across the hypothesis space.

Main Results:

  • The proposed method significantly improves performance when applied to margin disparity discrepancy and contrastive domain discrepancy.
  • Demonstrated enhanced effectiveness in unsupervised domain adaptation tasks.

Conclusions:

  • Warping probability discrepancy measures towards end tasks using bi-level optimization offers a more principled and effective approach.
  • The method provides substantial performance gains in unsupervised domain adaptation, highlighting its practical utility.