risk minimization
Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics
... the risk measure one seeks to ...Empirical Risk Minimization can be implemented using statistical counterparts of the risk based on much less terms (picked randomly by means of sampling with ...
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Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning
... We develop a framework for deriving sample complexity bounds using the max- imum likelihood principle for probabilistic grammars in a distribution-dependent setting. Distribution dependency is introduced here by making ...
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A Risk Minimization Framework for Extractive Speech Summarization
... Bayes risk has gained much attention and been applied with success to many natural language processing (NLP) tasks, such as automatic speech recogni- tion (Goel and Byrne, 2000), statistical machine translation ...
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A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification
... 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 general ...
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Classification Methods with Reject Option Based on Convex Risk Minimization
... 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 risk ...
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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 saddle ...
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Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
... We have developed a novel method for solving large-scale risk minimization problems. Our pro- posed optimized cutting plane algorithm (OCA) extends the standard CPA algorithm of Teo et al. (2007) by, first, ...
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Security Risk Minimization for Desktop and Mobile Software Systems. An In-Depth Analysis
... Abstract – In an extremely rapid growing industry such as the information technology nowadays, continuous and efficient workflows need to be established within any integrated enterprise or consumer software system. ...
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On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
... convex risk minimization methods for the problem of pattern ...convex risk minimization methods are the support vector machine, kernel logistic regression, AdaBoost, and least ...
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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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Training genetic programming classifiers by vicinal-risk minimization
... structural risk minimization (SRM) framework of Vapnik [25] has been a dominant paradigm in machine learning and has lead to the powerful notion of maximum margin classification as well as support-vector ...
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The Common-directions Method for Regularized Empirical Risk Minimization
... 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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Batch Learning from Logged Bandit Feedback through Counterfactual Risk Minimization
... Counterfactual risk minimization serves as a robust principle for designing algorithms that can learn from a batch of bandit feedback ...the risk estimator, and derive a generalization error bound ...
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Bundle Methods for Regularized Risk Minimization
... We would also like to point out connections to subgradient methods (Nedich and Bertsekas, 2000). These algorithms are designed for nonsmooth functions, and essentially choose an arbitrary element of the subgradient set ...
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Perturbation of convex risk minimization and its application in differential private learning algorithms
... Convex risk minimization is a commonly used setting in learning theory. In this paper, we firstly give a perturbation analysis for such algorithms, and then we apply this result to differential private ...
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Integrating risk minimization planning throughout the clinical development and commercialization lifecycle: an opinion on how drug development could be improved
... fulfill risk management commitments, thus minimizing the need to redirect internal ...the risk management function can serve to undermine the efforts of those internally delegated to coordinate the ...
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Stability Properties of Empirical Risk Minimization over Donsker Classes
... The empirical risk minimization (ERM) algorithm has been studied in learning theory to a great extent. Vapnik and Chervonenkis (1971, 1991) showed necessary and sufficient conditions for its consistency. In ...
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StructED: Risk Minimization in Structured Prediction
... Recently, several works proposed different surrogate loss functions which are closer to the task loss in some sense: structured ramp-loss (McAllester and Keshet, 2011), structured probit loss (Keshet et al., 2011), and ...
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Asymptotics in Empirical Risk Minimization
... In the statistical theory of classification, rates of convergence of empirical classifiers have been studied by a number of researchers, see for example Lugosi and Vayatis (2004), Lugosi[r] ...
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