Tag: Concept

A statistical principle stating that the average of a sequence of random variables converges to the expected value as the number of variables increases.

A generalization of the derivative for convex functions that allows for the analysis of nonsmooth optimization problems.

A function is Lipschitz continuous if there exists a constant such that the absolute difference in function values is bounded by this constant times the distance between input points.

No-regret guarantees ensure that the optimization algorithm performs nearly as well as the best fixed decision in hindsight, minimizing regret over time.

A method of representing functions or models as linear combinations of parameters.

A framework that encompasses a variety of flow matching and diffusion models for generating data.

A class of generative models that allow for the modification of existing data samples through a flow-based approach.

Points in a function where the function value is lower than at neighboring points, but not necessarily the lowest overall.

Functions that quantify the difference between predicted and actual outcomes in data mining tasks, often used to guide optimization.

A mathematical model used in statistical mechanics to describe ferromagnetism in statistical physics.