The rise of Online Social Networks (OSNs) has caused an insurmountable amount of interest from advertisers and researchers seeking to monopolize on its features. Researchers aim to develop strategies for determining how information is propagated among users within an OSN that is captured by diffusion or influence models. We consider the influence models for the IM-RO problem, a novel formulation to the Influence Maximization (IM) problem based on implementing Stochastic Dynamic Programming (SDP). In contrast to existing approaches involving influence spread and the theory of submodular functions, the SDP method focuses on optimizing clicks and ultimately revenue to advertisers in OSNs. Existing approaches to influence maximization have been actively researched over the past decade, with applications to multiple fields, however, our approach is a more practical variant to the original IM problem. In this paper, we provide an analysis on the influence models of the IM-RO problem by conducting experiments on synthetic and real-world datasets. We propose a Bayesian and Machine Learning approach for estimating the parameters of the influence models for the (Influence Maximization- Revenue Optimization) IM-RO problem. We present a Bayesian hierarchical model and implement the well-known Naive Bayes classifier (NBC), Decision Trees classifier (DTC) and Random Forest classifier (RFC) on three real-world datasets. Compared to previous approaches to estimating influence model parameters, our strategy has the great advantage of being directly implementable in standard software packages such as WinBUGS/OpenBUGS/JAGS and Apache Spark. We demonstrate the efficiency and usability of our methods in terms of spreading information and generating revenue for advertisers in the context of OSNs.

We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both populations. As a proof of concept, we implement our approach and empirically evaluate its ability to achieve both fairness and accuracy, using datasets from the fields of criminal risk assessment, credit, lending, and college admissions.

It isn't likely that any championships will change hands, though the matches in Columbus, Ohio will help advance WrestleMania storylines. Below are predictions for every match on the WWE Fastlane card. From the moment Shinsuke Nakamura won the Royal Rumble, it's been clear that he would face AJ Styles at WrestleMania in a rematch from Wrestle Kingdom 10. That means Styles will find a way to retain the title. Another WrestleMania feud could be born from the championship match, though don't be surprised to see Owens and Zayn team up after having their differences over the past few weeks.

In multi-instance (MI) learning, each object (bag) consists of multiple feature vectors (instances), and is most commonly regarded as a set of points in a multidimensional space. A different viewpoint is that the instances are realisations of random vectors with corresponding probability distribution, and that a bag is the distribution, not the realisations. In MI classification, each bag in the training set has a class label, but the instances are unlabelled. By introducing the probability distribution space to bag-level classification problems, dissimilarities between probability distributions (divergences) can be applied. The bag-to-bag Kullback-Leibler information is asymptotically the best classifier, but the typical sparseness of MI training sets is an obstacle. We introduce bag-to-class divergence to MI learning, emphasising the hierarchical nature of the random vectors that makes bags from the same class different. We propose two properties for bag-to-class divergences, and an additional property for sparse training sets.

Abstract--The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in which nodes have an associated vector of continuous attributes that are also used to learn the node-to-community assignments and corresponding SBM parameters. While this assumption is not realistic for every application, our model assumes that the attributes associated with the nodes in a network's community can be described by a common multivariate Gaussian model. In this augmented, attributed SBM, the objective is to simultaneously learn the SBM connectivity probabilities with the multivariate Gaussian parameters describing each community. While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes. This provides the flexibility in biological data to, for example, augment connectivity information with continuous measurements from multiple experimental modalities. Because the lack of labeled network data often makes community detection results difficult to validate, we highlight the usefulness of our model for two network prediction tasks: link prediction and collaborative filtering. As a result of fitting this attributed stochastic block model, one can predict the attribute vector or connectivity patterns for a new node in the event of the complementary source of information (connectivity or attributes, respectively). We also highlight two biological examples where the attributed stochastic block model provides satisfactory performance in the link prediction and collaborative filtering tasks. In various applications, each node in a network is equipped with additional information (or particular attributes) that was not implicitly taken into account in the construction of the network.

For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least squares estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this paper, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression. We first establish consistency and asymptotic normality of the estimator from a general subsampling algorithm, and then derive optimal subsampling probabilities that minimize the asymptotic mean squared error of the resultant estimator. An alternative minimization criterion is also proposed to further reduce the computational cost. The optimal subsampling probabilities depend on the full data estimate, so we develop a two-step algorithm to approximate the optimal subsampling procedure. This algorithm is computationally efficient and has a significant reduction in computing time compared to the full data approach. Consistency and asymptotic normality of the estimator from a two-step algorithm are also established. Synthetic and real data sets are used to evaluate the practical performance of the proposed method.

This article was posted by Manish Saraswat on Analytics Vidhya. Manish who works in marketing and Data Science at Analytics Vidhya believes that education can change this world. R, Data Science and Machine Learning keep him busy. Machine learning and data science are being looked as the drivers of the next industrial revolution happening in the world today. This also means that there are numerous exciting startups looking for data scientists.

This paper presents a comparison of six machine learning (ML) algorithms: GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, & Mangasarian, 1992) by measuring their classification test accuracy and their sensitivity and specificity values. The said dataset consists of features which were computed from digitized images of FNA tests on a breast mass (Wolberg, Street, & Mangasarian, 1992). For the implementation of the ML algorithms, the dataset was partitioned in the following fashion: 70% for training phase, and 30% for the testing phase. The hyper-parameters used for all the classifiers were manually assigned. Results show that all the presented ML algorithms performed well (all exceeded 90% test accuracy) on the classification task. The MLP algorithm stands out among the implemented algorithms with a test accuracy of ~99.04%.

Imagine a team of researchers looking for promising drug combinations to treat a specific cancer type for which current treatments are ineffective. The team has data on the effect of certain pairs of drugs on other cancer types, but the data are very sparse--only a few drug pairs have been tested on each cancer type, and each drug pair is tested in a few cancer types, at best, or has never been tested at all. The challenge is to select the most promising drug pairs for testing with the target cancer type, so as to minimize the cost associated to unsuccessful tests. We can formalize this challenge as the following inference problem: We have a partial observation of the pairwise interactions between a set of nodes (drugs) in different "network layers" (cancer types), and we need to infer which are the unobserved interactions within each layer (drug interactions in each cancer type). This challenge is relevant for the many systems that can be represented as multilayer networks [1-4], and is also formally analogous to the challenge of predicting the existence of interactions between nodes in time-resolved networks [5-11]. For instance, we would face the same situation if we had data about the daily email or phone communications between users, and wanted to infer the existence of interactions between pairs of users on a certain unobserved day; in this case each layer would be a different day. Here, we introduce new generative models that are suitable to address the challenge above. We model all layers concurrently, so that our approach takes full advantage of the information contained in all layers to make predictions for any one of them.

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems. During the last decade kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify existing kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency and spectral filtering properties. Our theoretical results provide valuable insights in assessing the advantages and limitations of existing pairwise learning methods.