[PDF] Top 20 Asymptotics in Empirical Risk Minimization
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Asymptotics in Empirical Risk Minimization
... In Section 2, we generalize the problem considered in Mohammadi and van de Geer (2003). It gives an application of the cube root asymptotics derived by Kim and Pollard (1990). We briefly explain the main idea of ... See full document
21
Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning
... structural risk minimization, for empirical risk minimization of prob- abilistic grammars using the ...out empirical risk minimization using this framework in both ... See full document
48
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 ... See full document
13
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 ... See full document
42
Ranking the Best Instances
... ranking rule (Cortes and Mohri, 2004; Freund et al., 2003; Rudin et al., 2005; Agarwal et al., 2005). In a previous work, we have mentioned that the bipartite ranking problem under the AUC crite- rion could be ... See full document
29
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 ... See full document
20
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 ... See full document
8
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 ... See full document
13
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. ... See full document
21
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
... characterizes the loss functions which lead to universally consistent classifiers and establishes uni- versal consistency for classifiers based on (3) and (4). Furthermore, he shows that there exist so- lutions of both ... See full document
28
Differentially Private Empirical Risk Minimization
... In this paper, we develop methods for approximating ERM while guaranteeing ε-differential privacy. Our results hold for loss functions and regularizers satisfying certain differentiability and convexity conditions. An ... See full document
41
Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics
... Many problems in machine learning, data mining and pattern recognition (such as the clus- tering problem described above) rely on a metric to measure the distance between data points. Choosing an appropriate metric for ... See full document
36
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
... the risk minimization ...the risk-minimizing solution varies with the misclassification cost ...the empirical loss. Coupled with empirical results on several real-world data sets, we ... See full document
20
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 ... See full document
321
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 ... See full document
30
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 ... See full document
49
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 ... See full document
19
Classification Methods with Reject Option Based on Convex Risk Minimization
... excess risk in the form of (1), followed by applications to the popular loss ...the empirical risk minimizer b f n that minimizes the empirical risk Q n ( f ... See full document
20
Data-Dependent Analysis of Learning Algorithms
... the Empirical Risk Minimization algorithm appeared as part of a conference paper with Peter Bartlett and Shahar Mendelson [3], and the optimality results are work in progress and contained in an ... See full document
12
Minimization of Portfolio Risk using Three Different Methods (A Comparative Study)
... Portfolio risk plays an important role in stock market decisions. This paper considers an alternative idea which is to compute the risk assuming fixed return. Three different methods used to study this ... See full document
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