Almost-everywhere Algorithmic Stability And Generalization Error

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Almost-everywhere algorithmic stability and generalization error Samuel Kutin Partha Niyogiy March 25, 2002 Abstract We introduce a new notion of algorithmic.

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Optimization is often difficult to apply to robots due to the presence of errors in model parameters, which can cause constraints to be violated during execution on.

We explore in some detail the notion of algorithmic stability as a viable framework for analyzing the generalization error of learning algorithms. We introduce the new notion of training stability of a learning algorithm and show that, in.

Algorithm. errors of deep learning. In this paper, we study the generalization errors of Stochastic Gradient Langevin Dynamics (SGLD) with non-convex objectives. Two theories are proposed with non-asymptotic discrete-time analysis,

UAI2002 KUTIN & NIYOGI 275 Almost-everywhere algorithmic stability and generalization error Samuel Kutin' and Partha Niyogit Abstract We explore in some detail the.

Nov 15, 2005. Y. Freund, Boosting a weak learning algorithm by majority. Almost-everywhere algorithmic stability and generalization error, in UAI-2002:.

Almost-everywhere algorithmic stability and generalization error Samuel Kutin and Partha Niyogi† Abstract We explore in some detail the notion of algorith-

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We explore in some detail the notion of algorithmic stability as a viable framework for analyzing the generalization error of learning algorithms. We introduce the.

http://www.c3.lanl.gov/ml/pubs_select.shtml. Kutin, S., & Niyogi, P. (2002). Almost-everywhere algorithmic stability and generalization error. In Proc. of uncertainty in artificial intelligence. Edmonton, August. Google Scholar.

Stability and Generalization of Bipartite Ranking Algorithms. COLT 2005:. Partha Niyogi: Almost-everywhere Algorithmic Stability and Generalization Error.

dc.description.abstract This thesis studies two key properties of learning algorithms: their generalization ability and.

Jun 10, 2014. Take a look at the Kutin-Niyogi extension of McDiarmid's inequality: "Almost- Everywhere Algorithmic Stability and Generalization Error", UAI.

Cluster Stability for Finite Samples – The Faculty of Mathematics and. – A clustering algorithm A is a function from any finite sample S ⊆ X, to some clustering CX of. Almost-everywhere algorithmic stability and generalization error.

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