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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Co-Learning Bayesian Optimization.

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    This study introduces co-learning Bayesian optimization (CLBO), a novel algorithm that uses multiple Gaussian process models to improve surrogate accuracy and avoid suboptimal solutions in black-box problems, enhancing global optimization efficiency.

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    Area of Science:

    • Optimization
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Bayesian optimization (BO) is sample efficient for black-box problems but can get stuck in suboptimal solutions due to poor Gaussian process (GP) surrogate accuracy.
    • The accuracy of GP surrogates is particularly challenging in regions near optimal solutions.

    Purpose of the Study:

    • To resolve the suboptimal problem in BO by improving surrogate accuracy.
    • To develop a novel BO algorithm that enhances global optimization efficiency using limited samples.

    Main Methods:

    • Proposed a co-learning Bayesian optimization (CLBO) algorithm that utilizes multiple complementary GP models instead of a single surrogate.
    • Leveraged both model diversity and agreement on unlabeled information, inspired by co-training algorithms and Rademacher complexity theory.
    • Evaluated CLBO on five numerical toy problems and three engineering benchmarks.

    Main Results:

    • CLBO effectively improves overall surrogate accuracy with limited samples.
    • The proposed method demonstrates enhanced global optimization efficiency compared to traditional BO.
    • Effectiveness was validated across diverse numerical and engineering test cases.

    Conclusions:

    • Co-learning Bayesian optimization (CLBO) offers a promising approach to overcome the limitations of traditional BO.
    • Exploiting model diversity and agreement is key to achieving higher surrogate accuracy and more efficient optimization.
    • CLBO presents a robust solution for complex black-box optimization tasks.