No-Regret Guarantees
No-regret guarantees ensure that the optimization algorithm performs nearly as well as the best fixed decision in hindsight, minimizing regret over time.
Category: Concepts
ECPv2
ECPv2 is a scalable algorithm designed for the global optimization of Lipschitz-continuous functions with unknown Lipschitz constants.
X1-Predictor Schemes
Predictive models that focus on estimating the state of a system at a future time point, often denoted as X1.
Time-dependent Loss Weighting
A training technique where the loss function is adjusted based on the time variable to stabilize model training.
Linear Parameterization
A method of representing functions or models as linear combinations of parameters.
Generator Matching
A framework that encompasses a variety of flow matching and diffusion models for generating data.
Edit Flows
A class of generative models that allow for the modification of existing data samples through a flow-based approach.
Ensemble Methods
Techniques that combine multiple models to improve overall performance and robustness in machine learning tasks.
Global Minima
Points in a function where the function value is lower than at neighboring points, but not necessarily the lowest overall.
Data Mining Losses
Functions that quantify the difference between predicted and actual outcomes in data mining tasks, often used to guide optimization.