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This paper investigates scenarios where adding correctly labeled data can harm model performance, introducing insertion-stability and examining the limits of dimension-based theory in machine learning generalization.
This paper answers an open question from Hanneke, Moran, and Waknine by showing that the agnostic PAC learning curve of a direct sum is not determined solely by the single-instance learning curve and the number of factors, providing a rate separation.
This paper studies risk-sensitive reinforcement learning in finite discounted MDPs with a generative model, focusing on the sample complexity of learning optimal value functions and policies under the optimized certainty equivalent (OCE) risk measure. It provides exact conditions for PAC-learnability, analyzes a model-based approach, and establishes tight lower bounds, including an improved dependence on the risk parameter for CVaR.