Empirical risk minimization under weak supervision
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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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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Excess risk bounds in robust empirical risk minimization
... The approach that we propose is based on replacing the sample mean that is at the core of ERM by a more “robust” estimator of E `pf pXqq that exhibits tight concentration under minimal moment assumptions. Well ...
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Risk minimization in stochastic volatility models: model risk and empirical performance
... The empirical performance of locally risk-minimizing delta hedges is tested using ...the under- lying and volatility which manifests itself as a skew in implied volatilities across strikes, and here ...
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Empirical risk minimization for metric learning using privileged information
... threshold under the empiri- cal risk minimization ...the empirical loss pe- nalizing the difference between the distance in the original space and that in the privileged ...
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Stability Properties of Empirical Risk Minimization over Donsker Classes
... Laboratory (CSAIL), as well as in the Dipartimento di Informatica e Scienze dell’Informazione (DISI) at University of Genoa, Italy. This research was sponsored by grants from: Office of Naval Research (DARPA) Contract ...
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Strengthening weak supervision for information retrieval
... In the context of generalized linear models, statistical leverage and Cook’s distance [Cook and Weisberg, 1982] from classical statistics describe how much im- pact a specific training point has on the learned parameters ...
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A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification
... their empirical risk function. [22] proposed an Area Under Curve (AUC) optimization method for multibiomarker panel identification named Nearest Centroid Classifier for AUC optimization ...the ...
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Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
... We can see from Algorithm 4 and 5 that, both of the computation and communication cost of each user will be O(d 2 ) = O( α 1 6 ). So, our question is, can we reduce these costs just as in the Section 4? We will leave it ...
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Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
... The consistency of classification algorithm plays a central role in statistical learning theory. A consistent algorithm guarantees us that taking more samples essentially suffices to roughly recon- struct the unknown ...
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Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
... sublinear convergence rates. However, their cost per iteration is O(nd) instead of O(d). Suzuki (2014) considered a problem similar to (1), but with more complex regularization function g, meaning that g does not have a ...
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Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics
... the risk is accurately estimated by U-statistics of degree d ≥ 1, ...such empirical risks can be replaced by drastically computationally simpler Monte-Carlo estimates based on O(n) terms only, usually ...
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Optimal risk minimization of Australian energy and mining portfolios under multiple measures of risk
... the empirical covariance matrix of the log returns is used, the weights from the QP and DE are not very ...weights under QPRL and QPAL and under DERL and DEAL are also quite ...the empirical ...
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Empirical Asset Pricing and Statistical Power in the Presence of Weak Risk Factors
... spurious risk factors, that is risk factors that are uncorrelated with the ...study risk factors that are mean zero by ...measure under the two model normalizations discussed ...a risk ...
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Local risk-minimization under illiquidity and consistent specification of credit migration models
... credit risk model framework together with the basic model ingredients and ...the weak consistency condition is presented and given in terms of an extended HJM no-arbitrage drift con- ...the risk-free ...
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EMPIRICAL RISK MINIMIZATION FOR CAR INSURANCE DATA
... On robust properties of convex risk minimization methods for pattern recognition. Support Vector Machines, Regulariza- tion, Optimization, and Beyond[r] ...
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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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Efficient Methods For Large-Scale Empirical Risk Minimization
... loss minimization suggests that ERM problems have more structure than FSM ...loss minimization to achieve lower overall computational complexity for a broad class of first-order methods applied to ...
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SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
... Abstract We propose a new algorithm for minimizing reg- ularized empirical loss: Stochastic Dual Newton Ascent (SDNA). Our method is dual in nature: in each iteration we update a random subset of the dual ...
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Weak Supervision for Learning Discourse Structure
... 1 IRIT, 2 Linagora, 3 CNRS, 4 ANITI {sonia.badene,kate.thompson,nicholas.asher}@irit.fr, {sbadene,jplorre}@linagora.com Abstract This paper provides a detailed comparison of a data programming approach with (i) ...
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