**AccurateML**

The growing demands of processing massive datasets have promoted irresistible trends of running machine learning applications on MapReduce. When processing large input data, it is often of greater values to produce fast and accurate enough approximate results than slow exact results. Existing techniques produce approximate results by processing parts of the input data, thus incurring large accuracy losses when using short job execution times, because all the skipped input data potentially contributes to result accuracy. We address this limitation by proposing AccurateML that aggregates information of input data in each map task to create small aggregated data points. These aggregated points enable all map tasks producing initial outputs quickly to save computation times and decrease the outputs’ size to reduce communication times. Our approach further identifies the parts of input data most related to result accuracy, thus first using these parts to improve the produced outputs to minimize accuracy losses. We evaluated AccurateML using real machine learning applications and datasets. The results show: (i) it reduces execution times by 30 times with small accuracy losses compared to exact results; (ii) when using the same execution times, it achieves 2.71 times reductions in accuracy losses compared to existing approximate processing techniques. … **Targeted Maximum Likelihood Estimation (TMLE)**

Maximum likelihood estimation fits a model to data, minimizing a global measure, such as mean squared error (MSE). When we are interested in one particular parameter of the data distribution and consider the remaining parameters to be nuisance parameters, we would prefer an estimate that has smaller bias and variance for the targeted parameter, at the expense of increased bias and/or variance in the estimation of nuisance parameters. Targeted maximum likelihood estimation targets the MLE estimate of the parameter of interest in a way that reduces bias. This bias reduction is sometimes accompanied by an increase in the variance of the estimate, but the procedure often reduces variance as well in finite samples. Asymptotically, TMLE is maximally efficient when the model and nuisance parameters are correctly specified.

The framework of targeted maximum likelihood estimation (TMLE), introduced in van der Laan & Rubin (2006), is a principled approach for constructing asymptotically linear and efficient substitution estimators in rich infinite-dimensional models. The mechanics of TMLE hinge upon first-order approximations of the parameter of interest as a mapping on the space of probability distributions. For such approximations to hold, a second-order remainder term must tend to zero sufficiently fast. In practice, this means an initial estimator of the underlying data-generating distribution with a sufficiently large rate of convergence must be available — in many cases, this requirement is prohibitively difficult to satisfy.

http://…/paper335 … **Hogwild!**

Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently pro- posed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and im- plementation that SGD can be implemented without any locking. We present an update scheme called Hogwild! which allows processors access to shared memory with the possibility of over- writing each other’s work. …

# If you did not already know

**12**
*Sunday*
Nov 2017

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