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KENDO achieves up to 5x faster Bayesian optimization and 27x faster active learning while enhancing predictive performance.
LLM-guided program discovery gets a serious boost: evolving programs across related tasks isn't just faster, it also beats overfitting, especially when data is scarce.
Achieve interpretability in continual learning without sacrificing accuracy: CI-CBM outperforms existing interpretable methods by 36% while matching black-box model performance.
