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Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over many user devices, and the learning algorithm must run on-device, on an arbitrary communication network, without a central coordinator. We propose COLA, a new decentralized training algorithm with strong theoretical guarantees and superior practical performance. Our framework overcomes many limitations of existing methods, and achieves communication efficiency, scalability, elasticity as well as resilience to changes in data and participating devices.

Logistic regression model is one of the models which often used in ML. Logistic regression can be phrased as latent variable models with error variables distributed as Gumbel distributions (type I generalized extreme value distributions). This phrasing is common in the theory of discrete choice models, which include logit models, probit models, and various extensions of them, and derives from the fact that the difference of two type-I GEV-distributed variables follows a logistic distribution, of which the logit function is the quantile function. The type-I GEV distribution thus plays the same role in these logit models as the normal distribution does in the corresponding probit models. GEV methods widely used in the treatment of tail risks and QQ-POT-QRM- extemeStat and more of these descriptions based on GEV.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

Boosting refers to a type of classification and regression algorithms that enjoy large popularity due to their excellent predictive accuracy on a wide range of datasets. The first boosting algorithms for classification, including the well known AdaBoost algorithm, were introduced by Schapire [1990], Freund and Schapire [1995], and Freund et al. [1996]. Later, several authors [Breiman, 1998, 1999, Friedman et al., 2000, Mason et al., 2000, Friedman, 2001] introduced the statistical view of boosting as a stagewise optimization approach. In particular, Friedman et al. [2000] first introduced boosting algorithms which iteratively optimize Bernoulli and multinomial likelihoods for binary and multiclass classification using Newton updates. Further, Friedman [2001] presented gradient descent based boosting algorithms for both regression and classification tasks with general loss functions.

In this paper we introduce a new machine learning (ML) model for nonlinear regression called Boosting Smooth Transition Regression Tree (BooST). The main advantage of the BooST is that it estimates the derivatives (partial effects) of very general nonlinear models, providing more interpretation than other tree based models concerning the mapping between the covariates and the dependent variable. We provide some asymptotic theory that shows consistency of the partial derivatives and we present some examples on simulated and empirical data.

This database provided a comprehensive record of opioids dispensed at California pharmacies to civilian, non–U.S. Department of Veterans Affairs, and non-institutionalized patients treated by clinicians in our sample. Descriptive and inferential statistics were carried out with the Stata software (6). The cmp command in Stata was used to compute a difference-in-differences estimator within a mixed-model two-part linear regression analysis (7). The difference-in-differences estimator compared the average change over time in milligram morphine equivalents (MMEs) dispensed for prescribers in the intervention group with the average change over time for prescribers in the control group.

Our initial results look promising, but there is room for improvement. We made a number of choices and assumptions for our initial analysis. Our next steps would be to go back and evaluate these to determine what changes we can make to tune our classifier. If we plan to use this classifier for a real-time credit card fraud detection system, we want to ensure that we can catch all the fraudulent transactions and also keep our customers happy by correctly identifying non-fraudulent transactions. Once we have a good classifier, we can use it directly with transactions arriving into Ignite in real-time. Additionally, with Ignite's continuous learning capabilities, we can refine and tune our classifier further with new data, as the data arrive. Finally, using Ignite as the basis for a real-time fraud detection system enables us to obtain many advantages, such as the ability to scale ML processing beyond a single node, the storage and manipulation of massive quantities of data, and zero ETL.

We present a regression technique for data driven problems based on polynomial chaos expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ), where it is typically used to replace a runnable but expensive computational model subject to random inputs with an inexpensive-to-evaluate polynomial function. The metamodel obtained enables a reliable estimation of the statistics of the output, provided that a suitable probabilistic model of the input is available. In classical machine learning (ML) regression settings, however, the system is only known through observations of its inputs and output, and the interest lies in obtaining accurate pointwise predictions of the latter. Here, we show that a PCE metamodel purely trained on data can yield pointwise predictions whose accuracy is comparable to that of other ML regression models, such as neural networks and support vector machines. The comparisons are performed on benchmark datasets available from the literature. The methodology also enables the quantification of the output uncertainties and is robust to noise. Furthermore, it enjoys additional desirable properties, such as good performance for small training sets and simplicity of construction, with only little parameter tuning required. In the presence of statistically dependent inputs, we investigate two ways to build the PCE, and show through simulations that one approach is superior to the other in the stated settings.

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.