The spread of online reviews and opinions and its growing influence on people's behavior and decisions, boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this paper, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case.

We investigate the construction of early stopping rules in the nonparametric regression problem where iterative learning algorithms are used and the optimal iteration number is unknown. More precisely, we study the discrepancy principle, as well as modifications based on smoothed residuals, for kernelized spectral filter learning algorithms including gradient descent. Our main theoretical bounds are oracle inequalities established for the empirical estimation error (fixed design), and for the prediction error (random design). From these finite-sample bounds it follows that the classical discrepancy principle is statistically adaptive for slow rates occurring in the hard learning scenario, while the smoothed discrepancy principles are adaptive over ranges of faster rates (resp. higher smoothness parameters). Our approach relies on deviation inequalities for the stopping rules in the fixed design setting, combined with change-of-norm arguments to deal with the random design setting.

Finite mixture models are an important tool in the statistical analysis of data, for example in data clustering. The optimal parameters of a mixture model are usually computed by maximizing the log-likelihood functional via the Expectation-Maximization algorithm. We propose an alternative approach based on the theory of Mean Field Games, a class of differential games with an infinite number of agents. We show that the solution of a finite state space multi-population Mean Field Games system characterizes the critical points of the log-likelihood functional for a Bernoulli mixture. The approach is then generalized to mixture models of categorical distributions. Hence, the Mean Field Games approach provides a method to compute the parameters of the mixture model, and we show its application to some standard examples in cluster analysis.

Estimation of Gaussian graphical models is important in natural science when modeling the statistical relationships between variables in the form of a graph. The sparsity and clustering structure of the concentration matrix is enforced to reduce model complexity and describe inherent regularities. We propose a model to estimate the sparse Gaussian graphical models with hidden clustering structure, which also allows additional linear constraints to be imposed on the concentration matrix. We design an efficient two-phase algorithm for solving the proposed model. We develop a symmetric Gauss-Seidel based alternating direction method of the multipliers (sGS-ADMM) to generate an initial point to warm-start the second phase algorithm, which is a proximal augmented Lagrangian method (pALM), to get a solution with high accuracy. Numerical experiments on both synthetic data and real data demonstrate the good performance of our model, as well as the efficiency and robustness of our proposed algorithm.

Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Due to its ability to cope with the real-world objects with multiple semantic meanings, multi-label learning has been successfully applied in various application domains [1], such as tag recommendation [2, 3], bioinformatics [4, 5, 6], information retrieval [7, 8], rule mining [9, 10], web mining [11, 12], and so on. Formally speaking, suppose the given multi-label data set is denoted by D {x i, y i } n i 1 where x i R d is a feature vector with d dimensions (features) and y i { 1, 1} q is the corresponding label vector with the size of label space being q. Here, y ij 1 indicates that the i-th instance x i has the j-th label (or equivalently, the j-th label is a relevant label of x i), otherwise the j-th label is an irrelevant label of x i . Let X R d be the d-dimensional feature space, and Y { 1, 1} q be the q-dimensional label space, multi-label learning aims to induce a mapping function f: X Y, which is able to correctly predict the label vector of unseen instances. To solve the multi-label learning problem, the most straightforward solution is Binary Relevance (BR) [13, 14], which aims to decompose the original learning problem into a set of independent binary classification problems. However, this solution generally achieves mediocre performance, as label correlations are regrettably ignored. To ease this problem, a large number of multi-label learning approaches take into account label correlations explicitly or implicitly to improve the learning performance.

When a user connects to the Internet to fulfill his needs, he often encounters a huge amount of related information. Recommender systems are the techniques for massively filtering information and offering the items that users find them satisfying and interesting. The advances in machine learning methods, especially deep learning, have led to great achievements in recommender systems, although these systems still suffer from challenges such as cold-start and sparsity problems. To solve these problems, context information such as user communication network is usually used. In this paper, we have proposed a novel recommendation method based on Matrix Factorization and graph analysis methods, namely Louvain for community detection and HITS for finding the most important node within the trust network. In addition, we leverage deep Autoencoders to initialize users and items latent factors, and the Node2vec deep embedding method gathers users' latent factors from the user trust graph. The proposed method is implemented on Ciao and Epinions standard datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing state-of-the-art recommendation methods. Our approach outperforms other comparative methods and achieves great improvements, i.e., 15.56% RMSE improvement for Epinions and 18.41% RMSE improvement for Ciao.

A yuru-chara is a mascot character created by local governments and companies for publicizing information on areas and products. Because it takes various costs to create a yuruchara, the utilization of machine learning techniques such as generative adversarial networks (GANs) can be expected. In recent years, it has been reported that the use of class conditions in a dataset for GANs training stabilizes learning and improves the quality of the generated images. However, it is difficult to apply class conditional GANs when the amount of original data is small and when a clear class is not given, such as a yuruchara image. In this paper, we propose a class conditional GAN based on clustering and data augmentation. Specifically, first, we performed clustering based on K-means++ on the yuru-chara image dataset and converted it into a class conditional dataset. Next, data augmentation was performed on the class conditional dataset so that the amount of data was increased five times. In addition, we built a model that incorporates ResBlock and self-attention into a network based on class conditional GAN and trained the class conditional yuru-chara dataset. As a result of evaluating the generated images, the effect on the generated images by the difference of the clustering method was confirmed.

This paper documents the consequences of the identification failures for a class of linear ill-posed inverse models. The Tikhonov-regularized estimator converges to a well-defined limit equal to the best approximation of the structural parameter in the orthogonal complement to the null space of the operator. We illustrate that in many cases the best approximation may coincide with the structural parameter or at least may reasonably approximate it. We characterize the nonasymptotic Hilbert space norm and the uniform norm convergence rates for the best approximation. Nonidentification has important implications for the large sample distribution of the Tikhonov-regularized estimator, and we document the transition between the Gaussian and the weighted chi-squared limits. The theoretical results are illustrated for the nonparametric IV and the functional linear IV regressions and are further supported by the Monte Carlo experiments.

Traumatic brain injury (TBI) is a common cause of disability and death in young people [1]. Caused by external impact such as blunt trauma, penetrating objects or blast waves to the head, TBI is becoming increasingly prevalent with an estimated 1.6 million individuals sustaining mild traumatic brain injury (mTBI) each year. Major causes of TBI have been vehicle related collisions, sports or combat injuries causing brain damages, including tearing injuries of white matter or hematomas resulting in nausea, disturbed sleep patterns [2], dizziness, memory and/or concentration problems, emotional disturbances and seizures. Many of whom are never hospitalized and may suffer from consequences of head injury. Lack of consensus regarding what constitutes mTBI adds to the complication of the under-diagnosis of the disease [3].

Artificial Intelligence or Machine Learning has proven to be game-changers that transformed the digital landscape. They open boundless opportunities for online businesses and help the businesses grow by improving productivity, simplifying processes and optimizing technical efforts. Python, C, Java, LISP, and Prolog are among the preferred technologies that can be used for implementing AI-based projects. At SynergyTop, we prefer Python and consider it the most optimum choice for building AI and ML solutions. Python is simple and easy to learn and is a preferred choice for developers across the globe.