Extends PAC machine teaching to handle deductive errors by requiring teachers to select sets that lead to approximately correct hypotheses with high probability despite learner mistakes, with complexity results and LLM experiments.
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cs.LG 2years
2026 2representative citing papers
ITBoost uses MDL-based complexity of residual trajectories to assign trust weights, improving robustness to label noise in tabular boosting without sacrificing clean-data performance.
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Teaching and Learning under Deductive Errors
Extends PAC machine teaching to handle deductive errors by requiring teachers to select sets that lead to approximately correct hypotheses with high probability despite learner mistakes, with complexity results and LLM experiments.
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ITBoost: Information-Theoretic Trust for Robust Boosting
ITBoost uses MDL-based complexity of residual trajectories to assign trust weights, improving robustness to label noise in tabular boosting without sacrificing clean-data performance.