A constrained density-ratio network approximates the Radon-Nikodym derivative and feeds an anytime PAC-Bayes certificate for learning under covariate shift, validated via synthetic patch tests and real-data deployment.
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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 PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift
A constrained density-ratio network approximates the Radon-Nikodym derivative and feeds an anytime PAC-Bayes certificate for learning under covariate shift, validated via synthetic patch tests and real-data deployment.
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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.