Tag
The paper proposes A*-Inspired Batch Selection (A*-BS), a lightweight strategy that treats mini-batch scheduling as a heuristic search to improve CNN training efficiency. On MedMNIST tasks, a simple CNN with A*-BS outperforms deeper ResNet-18/50 baselines in accuracy and training speed.
This paper provides a unified theoretical framework for pseudo observation batch Bayesian optimization, proving that Gaussian processes produce distinct batch points and that common methods like Constant Liar and Kriging Believer are instances of a single conditioning mechanism. It introduces the Structural Diversity Diagnostic (SDD) for testing surrogate compatibility and validates predictions across multiple benchmark functions and hyperparameter tuning.