Convergence
Convergence in machine learning is the point at which an iterative optimization algorithm reaches a stable solution, meaning the loss function stops decreasing meaningfully and further parameter updates yield…
Training & Optimization
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Convex Function
A convex function is a real-valued function whose graph curves upward into a bowl or cup shape, so that the line segment (chord) connecting any two points on the graph lies on or above the graph itself.
Convex Optimization
Convex optimization is the branch of mathematical optimization that minimizes a convex function over a convex set, a problem class with one defining advantage: every local minimum is also a global minimum.
Convex Set
A convex set is a set of points in which the line segment connecting any two points of the set lies entirely within the set .
Distributionally Robust Optimization
Distributionally robust optimization (DRO) is a framework for making decisions when the probability distribution of uncertain inputs is not known exactly.
Gradient
In machine learning, the gradient is the vector of partial derivatives of a loss function with respect to every model parameter, and it points in the direction in which the loss increases most steeply.
Log Loss
Log loss is the negative log-likelihood of the predicted probabilities and the standard loss function for probabilistic classification: for binary labels it is computed as…
Minimax Loss
Minimax loss is a loss function rooted in game theory and decision theory that measures the worst-case performance of a strategy, algorithm, or model.
Wasserstein Loss
Wasserstein loss is a loss function for training generative models that measures the distance between two probability distributions as the Wasserstein-1 distance