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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Appetitive Associative Olfactory Learning in Drosophila Larvae
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U-processes and preference learning.

Hong Li1, Chuanbao Ren, Luoqing Li

  • 1School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China hongli@mail.hust.edu.cn.

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Summary
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This study introduces a novel preference learning framework using U-processes and Rademacher complexity. It establishes theoretical bounds for excess risk, improving convergence rates for pairwise preference algorithms.

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Area of Science:

  • Machine Learning
  • Statistical Learning Theory

Background:

  • Preference learning is a significant area in machine learning.
  • Existing methods often lack robust theoretical guarantees for pairwise loss functions.

Purpose of the Study:

  • To propose a novel learning framework for pairwise loss functions.
  • To establish theoretical underpinnings for preference learning algorithms using U-processes.

Main Methods:

  • Empirical risk minimization of U-processes.
  • Rademacher complexity for generalization bounds.
  • Uniform Bernstein inequality for U-processes of degree 2.
  • Entropy methods and peeling skills for risk estimation.

Main Results:

  • Established a uniform Bernstein inequality for U-processes.
  • Derived bounds for excess risk using the established inequality.
  • Achieved convergence rate derivations for pairwise preference learning algorithms.

Conclusions:

  • The proposed framework provides a theoretically sound approach to pairwise preference learning.
  • The derived convergence rates offer improved performance guarantees for algorithms utilizing squared and indicator losses.