Letter to the Editor—An Optimal Stopping Rule

Early Stopping and Non-parametric Regression: An Optimal Data-dependent Stopping Rule

Early stopping is a form of regularization based on choosing when to stop running an iterative algorithm. Focusing on non-parametric regression in a reproducing kernel Hilbert space, we analyze the early stopping strategy for a form of gradient-descent applied to the least-squares loss function. We propose a data-dependent stopping rule that does not involve hold-out or cross-validation data, a...

Stopping Rule Use

Information search is critical in most decision-making tasks. An important aspect of information search is the stopping rule used by the decision maker to terminate information acquisition. Decision-making problems may be usefully decomposed into design problems and choice problems. The distinction is critical because the goals of stopping behavior in the two types of problems are quite differe...

Letter to the editor

Letter to the editor

 This article has no abstract.

The Problem of Optimal Stopping

The problem of Optimal Stopping is of fundamental importance for sequential analysis, for the detection of signals in a background of noise, and also for pricing American-type options in the modern theory of finance. We shall review in this talk two relatively recent approaches to this problem: the deterministic or “pathwise” approach of Davis & Karatzas (1994), and the “integral representation...