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📚TheoryAdvanced

Generalization Bounds for Deep Learning

Generalization bounds explain why deep neural networks can perform well on unseen data despite having many parameters.

#generalization bounds#pac-bayes#compression bounds+12
📚TheoryIntermediate

Concentration Inequalities

Concentration inequalities give high-probability bounds that random outcomes stay close to their expectations, even without knowing the full distribution.

#concentration inequalities
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#generalization bounds
#hoeffding inequality
#chernoff bound
+12
📚TheoryAdvanced

Statistical Learning Theory

Statistical learning theory explains why a model that fits training data can still predict well on unseen data by relating true risk to empirical risk plus a complexity term.

#statistical learning theory#empirical risk minimization#structural risk minimization+11
📚TheoryIntermediate

PAC Learning

PAC learning formalizes when a learner can probably (with probability at least 1−δ) and approximately (error at most ε) succeed using a polynomial number of samples.

#pac learning#agnostic learning#vc dimension+12
📚TheoryAdvanced

Rademacher Complexity

Rademacher complexity is a data-dependent measure of how well a function class can fit random noise on a given sample.

#rademacher complexity#empirical rademacher#generalization bounds+12