empirical risk minimization
Stability Properties of Empirical Risk Minimization over Donsker Classes
... The empirical risk minimization (ERM) algorithm has been studied in learning theory to a great ...from empirical process theory have been successfully applied, and, in particular, it has been ...
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Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning
... This article proceeds as follows. In Section 2 we review the background necessary from Vapnik’s (1988) empirical risk minimization framework. This framework is re- duced to maximum likelihood ...
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A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification
... using empirical risk minimization to determine the communication cost which is independent of the data size, and is only weakly dependent on the number of machines and then designed and implemented a ...
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Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics
... the risk measure one seeks to ...problems, Empirical Risk Minimization can be implemented using statistical counterparts of the risk based on much less terms (picked randomly by means ...
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Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization
... We show that the average stability notion introduced by Kearns and Ron (1999); Bousquet and Elisseeff (2002) is invariant to data preconditioning, for a wide class of generalized linear models that includes most of the ...
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The Common-directions Method for Regularized Empirical Risk Minimization
... regularized empirical risk minimization (ERM) of linear models, the method comes with little additional cost by wisely caching the inner products between these p i and the training ...
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Differentially Private Empirical Risk Minimization
... Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce ...
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Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
... We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictors. The problem structure allows us to reformulate it as a convex-concave ...
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Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
... the underlying distribution D. The goal of statistical learning is to find a classifier based on the samples and a pre-chosen set F of vector functions with K-components. For this purpose, a very successful method used ...
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Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View
... several empirical studies have revealed that non-convex loss functions can achieve better classification accuracy than convex loss functions (Nguyen and Sanner 2013), and recent developments in Deep Neural ...
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Asymptotics in Empirical Risk Minimization
... of empirical classifiers have been studied by a number of researchers, see for example Lugosi and Vayatis (2004), Lugosi and Nobel (1999), Lugosi and Wegkamp (2004), Koltchinskii and Panchenko (2002), Boucheron et ...
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Efficient Methods For Large-Scale Empirical Risk Minimization
... By proving lower and upper bounds on the approximate Hessians of the component func- tions it can be guaranteed that the sequence of iterates w t genearaed by RES converges to the optimal argument w ∗ with probability 1 ...
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Ranking the Best Instances
... We formulate a local form of the bipartite ranking problem where the goal is to focus on the best instances. We propose a methodology based on the construction of real-valued scoring functions. We study empirical ...
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Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees
... small empirical error on the set of growing data are viable candidates for the final ...an empirical risk minimization algorithm for ...
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Learning Theory Approach to Minimum Error Entropy Criterion
... We consider the minimum error entropy (MEE) criterion and an empirical risk minimization learn- ing algorithm when an approximation of R´enyi’s entropy (of order 2) by Parzen windowing is minimized. ...
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On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
... In statistical learning theory the principle of regularized empirical risk minimization based on con- vex loss functions plays an important role, see Vapnik (1998). One strong argument in favor of ...
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The use of vicinal-risk minimization for training decision trees
... vicinal risk minimization (VRM) for training decision trees to approximately maximize decision ...necessary minimization using an appropriate meta- heuristic (genetic programming) and present results ...
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Classification Methods with Reject Option Based on Convex Risk Minimization
... that empirical risk minimization easily becomes infeasible, the paper proposes minimizing convex risks based on surrogate convex loss ...excess risk can be bounded through the excess surrogate ...
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Learning Sparse Low-Threshold Linear Classifiers
... for empirical risk minimization which matches the online-to-batch guarantee above (up to logarithmic factors), and ensures a sample complexity of ˜ O(θk log(d)/ 2 ) also when using ...as ...
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Data-Dependent Analysis of Learning Algorithms
... the Empirical Risk Minimization algorithm ...for empirical minimizers by a direct analysis of the ERM ...of empirical minimizers in terms of the “complexity” of data-dependent local ...
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