Conceptconcentration inequality

Concentration Inequality

Concentration inequalities are fundamental tools in probability theory and statistical learning theory for characterizing upper bounds on the probability that a random variable deviates from its expectation. Common forms include Hoeffding's inequality, the Chernoff bound and Bernstein's inequality.

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Oct 22

This work extends classical concentration inequalities to the data-dependent setting, providing theoretical tools for scenarios such as reinforcement learning and sequential decision-making.

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This work extends classical concentration inequalities to the data-dependent setting, providing theoretical tools for scenarios such as reinforcement learning and sequential decision-making.

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Source Articles

Self-Normalized Bernstein Inequalities for Regenerative Processes: A New Tool for Learning from Dependent DataOct 22 A New Breakthrough in McDiarmid Inequalities for Heavy Tails: Reverse KL TensorizationOct 16