The bias-variance decomposition of error provides useful insights into the error performance of a classifier as it is applied to different types of learning task. Most notably, it has been used to explain the extraordinary effectiveness of ensemble learning techniques. It is important that the research community have effective tools for assessing such explanations. To this end, techniques have ...

The performance of federated learning systems is bottlenecked by communication costs and training variance. overhead problem usually addressed three communication-reduction techniques, namely, model compression, partial device participation, periodic aggregation, at the cost increased Different from traditional distributed systems, suffers data heterogeneity (since devices sample their possibly...

Chest clapping, vibration, and shaking were studied in 10 physiotherapists who applied these techniques on an anesthetized animal model. Hemodynamic variables (such as heart rate, blood pressure, pulmonary artery pressure, and right atrial pressure) were measured during the application of these techniques to verify claims of adverse events. In addition, expired tidal volume and peak expiratory ...

We propose techniques for accurate and computationally viable estimation of timing yield using circuit-level Monte Carlo simulation. Our techniques are based on well-known variance reduction approaches from Monte Carlo simulation literature. By adapting these techniques to the yield estimation problem, one can reduce the number of Monte Carlo samples required in order to estimate yield within a...

When assessing experimentally the performance of metaheuristic algorithms on a set of hard instances of an NP-complete problem, the required time to carry out the experimentation can be very large. A means to reduce the needed effort is to incorporate variance reduction techniques in the computational experiments. For the incorporartion of these techniques, the traditional approaches propose me...

Wavelet methods such as standard thresholding techniques are commonly used to estimate a nonparametric regression function from noisy sample data, under the traditional assumption that noises have constant variance. In situations where data have nonconstant variance, these standard techniques do not work well in estimating the regression function unless the heteroscedasticity is taken into acco...

The variance of a fuzzy random variable plays an important role as a measure of central tendency. Some of the main contributions in this topic are consolidated and discussed in this paper. In case of the hypothesis testing problem, bootstrap techniques (Efron and Tibshirani, 1993) have empirically been shown to be efficient and powerful. Algorithms to apply these techniques in practice and some...

Given a sequence of fractional frequency deviates, we investigate the relationship between the sample variance of these deviates and the total variance (Totvar) estimator of the Allan variance. We demonstrate that we can recover exactly twice the sample variance by renormalizing the Totvar estimator and then summing it over dyadic averaging times 1, 2, 4, . . . , 2 along with one additional ter...

This paper describes a method of matrix decomposition which retains the ability of factor analytic techniques to summarize data in terms of a relatively low number of coordinates; but at the same time, does not sacrifice the useful analysis of variance heuristic of partitioning data matrices into independent sources of variation which are relatively simple to interpret. The basic model is essen...