Concepts6
Concentration Inequalities
Concentration inequalities give high-probability bounds that random outcomes stay close to their expectations, even without knowing the full distribution.
Information-Theoretic Lower Bounds
Information-theoretic lower bounds tell you the best possible performance any learning algorithm can achieve, regardless of cleverness or compute.
Reinforcement Learning Theory
Reinforcement Learning (RL) studies how an agent learns to act in an environment to maximize long-term cumulative reward.
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.
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.
VC Dimension
VC dimension measures how many distinct labelings a hypothesis class can realize on any set of points of a given size.