Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Alex Kane has been on a roll in the second half of the year with Major League Wrestling. The Georgia native became the MLW World Heavyweight Champion in July with a win over Alex Hammerstone at "Never Say Never" and has successfully defended the title three times since the victory with his faction – the Bomaye Fight Club – behind him. Thursday night will be one of Kane's toughest matches yet as he steps into the ring against the "Indy God" Matt Cardona at "One Shot" in New York City.

This paper investigates the asymptotic distribution of the K-fold cross validation error in an i.i.d. setting. As the number of observations n goes to infinity while keeping the number of folds K fixed, the K-fold cross validation error is √ n-consistent for the expected out-of-sample error and has an asymptotically normal distribution. A consistent estimate of the asymptotic variance is derived and used to construct asymptotically valid confidence intervals for the expected out-of-sample error. A hypothesis test is developed for comparing two estimators’ expected out-of-sample errors and a subsampling procedure is used to obtain critical values. Monte Carlo simulations demonstrate the asymptotic validity of our confidence intervals for the expected out-of-sample error and investigate the size and power properties of our test. In our empirical application, we use our estimator selection test to compare the out-of-sample predictive performance of OLS, Neural Networks, and Random Forests for predicting the sale price of a domain name in a GoDaddy expiry auction.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. EC3 made his National Wrestling Alliance (NWA) debut at the company's 74th-anniversary show last year and a year later he defeated Tyrus for the Worlds Heavyweight Championship putting him on top of the historic promotion and ending the career of one of the most well-known performers in the business. Two months after capturing the title at the 75th-anniversary show, Thom Latimer used the "Lucky Seven Rule" to drop the NWA World Television Championship for a chance at EC3's title. The two will meet in a singles match at NWA Samhain later this month for the title. Better yet, EC3 gets to perform in front of his hometown fans in Cleveland, Ohio.

In a lengthy blog post last week, Turnitin Chief Product Officer Annie Chechitelli said the company wants to be transparent about its technology, but she didn't back off from deploying it. She said that for documents that its detection software thinks contain over 20 percent AI writing, the false positive rate for the whole document is less than 1 percent. But she didn't specify what the error rate is the rest of the time -- for documents its software thinks contain less than 20 percent AI writing. In such cases, Turnitin has begun putting an asterisk next to results "to call attention to the fact that the score is less reliable."

Algorithms and models are increasingly deployed to inform decisions about people, inevitably affecting their lives. As a consequence, those in charge of developing these models must carefully evaluate their impact on different groups of people and favour group fairness, that is, ensure that groups determined by sensitive demographic attributes, such as race or sex, are not treated unjustly. To achieve this goal, the availability (awareness) of these demographic attributes to those evaluating the impact of these models is fundamental. Unfortunately, collecting and storing these attributes is often in conflict with industry practices and legislation on data minimisation and privacy. For this reason, it can be hard to measure the group fairness of trained models, even from within the companies developing them. In this work, we tackle the problem of measuring group fairness under unawareness of sensitive attributes, by using techniques from quantification, a supervised learning task concerned with directly providing group-level prevalence estimates (rather than individual-level class labels). We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem, as they are robust to inevitable distribution shifts while at the same time decoupling the (desirable) objective of measuring group fairness from the (undesirable) side effect of allowing the inference of sensitive attributes of individuals. More in detail, we show that fairness under unawareness can be cast as a quantification problem and solved with proven methods from the quantification literature. We show that these methods outperform previous approaches to measure demographic parity in five experimental protocols, corresponding to important challenges that complicate the estimation of classifier fairness under unawareness.

When the probability distribution P(X, Y) is known, the optimal classifier, leading to the minimum misclassification rate, is given by the Maximum A-posteriori Probability (MAP) decision rule. However, in practice, estimating the complete joint distribution P(X, Y) is computationally and statistically impossible for large values of d. Therefore, an alternative approach is to first estimate some low order marginals of the joint probability distribution P(X, Y) and then design the classifier based on the estimated low order marginals. This approach is also helpful when the complete training data instances are not available due to privacy concerns. In this work, we consider the problem of finding the optimum classifier based on some estimated low order marginals of (X, Y).

One approach to improving the running time of kernel-based methods is to build a small sketch of the kernel matrix and use it in lieu of the full matrix in the machine learning task of interest. Here, we describe a version of this approach that comes with running time guarantees as well as improved guarantees on its statistical performance. By extending the notion of statistical leverage scores to the setting of kernel ridge regression, we are able to identify a sampling distribution that reduces the size of the sketch (i.e., the required number of columns to be sampled) to the effective dimensionality of the problem. This latter quantity is often much smaller than previous bounds that depend on the maximal degrees of freedom. We give an empirical evidence supporting this fact. Our second contribution is to present a fast algorithm to quickly compute coarse approximations to these scores in time linear in the number of samples.

We thank the reviewers for acknowledging our contributions and providing valuable comments. We'll further improve the paper in the final version. We address the detail comments below. To R1: Q1: Relation with variants of DS: Our main goal is to provide a discriminative max-margin formulation, which is general and complementary to generative methods. For example, though we consider the vanilla DS in CrowdSVM for both clarity and space limit, other variants (e.g., [15,11]) can be naturally incorporated, as the RegBayes formulation (9) is generally applicable to any Bayesian models. Finally, the spectral initialization method [23] for confusion matrices can also be used to initialize the confusion matrices in CrowdSVM, so as the methods in [12].

The F-measure is an important and commonly used performance metric for binary prediction tasks. By combining precision and recall into a single score, it avoids disadvantages of simple metrics like the error rate, especially in cases of imbalanced class distributions. The problem of optimizing the F-measure, that is, of developing learning algorithms that perform optimally in the sense of this measure, has recently been tackled by several authors. In this paper, we study the problem of F-measure maximization in the setting of online learning. We propose an efficient online algorithm and provide a formal analysis of its convergence properties. Moreover, first experimental results are presented, showing that our method performs well in practice.

Careful tuning of a regularization parameter is indispensable in many machine learning tasks because it has a significant impact on generalization performances. Nevertheless, current practice of regularization parameter tuning is more of an art than a science, e.g., it is hard to tell how many grid-points would be needed in cross-validation (CV) for obtaining a solution with sufficiently small CV error. In this paper we propose a novel framework for computing a lower bound of the CV errors as a function of the regularization parameter, which we call regularization path of CV error lower bounds. The proposed framework can be used for providing a theoretical approximation guarantee on a set of solutions in the sense that how far the CV error of the current best solution could be away from best possible CV error in the entire range of the regularization parameters. Our numerical experiments demonstrate that a theoretically guaranteed choice of a regularization parameter in the above sense is possible with reasonable computational costs.