A constrained density-ratio network with augmented-Lagrangian enforcement and anytime PAC-Bayes delivers generalization certificates for importance-weighted learning under covariate shift.
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Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.
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Anytime and Difficulty-Adaptive PAC-Bayes for Constrained Density-Ratio Network with Continual Learning Guarantees
A constrained density-ratio network with augmented-Lagrangian enforcement and anytime PAC-Bayes delivers generalization certificates for importance-weighted learning under covariate shift.
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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions
Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.