We consider a sparse multi-task regression framework for fitting a collection of related sparse models. Representing models as nodes in a graph with edges between related models, a framework that fuses lasso regressions with the total variation penalty is investigated. Under a form of restricted eigenvalue assumption, bounds on prediction and squared error are given that depend upon the sparsity of each model and the differences between related models. This assumption relates to the smallest eigenvalue restricted to the intersection of two cone sets of the covariance matrix constructed from each of the agents' covariances. We show that this assumption can be satisfied if the constructed covariance matrix satisfies a restricted isometry property. In the case of a grid topology high-probability bounds are given that match, up to log factors, the no-communication setting of fitting a lasso on each model, divided by the number of agents. A decentralised dual method that exploits a convex-concave formulation of the penalised problem is proposed to fit the models and its effectiveness demonstrated on simulations against the group lasso and variants.

Today, treatment effect estimation at the individual level is a vital problem in many areas of science and business. For example, in marketing, estimates of the treatment effect are used to select the most efficient promo-mechanics; in medicine, individual treatment effects are used to determine the optimal dose of medication for each patient and so on. At the same time, the question on choosing the best method, i.e., the method that ensures the smallest predictive error (for instance, RMSE) or the highest total (average) value of the effect, remains open. Accordingly, in this paper we compare the effectiveness of machine learning methods for estimation of individual treatment effects. The comparison is performed on the Criteo Uplift Modeling Dataset. In this paper we show that the combination of the Logistic Regression method and the Difference Score method as well as Uplift Random Forest method provide the best correctness of Individual Treatment Effect prediction on the top 30\% observations of the test dataset.

Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Multilabel Logistic Regression with Hidden variables (MLRH), which extends the standard logistic regression by introducing hidden variables. Hidden variables make it possible to go beyond the conventional multiclass logistic regression by relaxing the one-hot-encoding constraint. We define a new joint distribution of labels and hidden variables which enables us to obtain one classifier for multilabel classification. Our experimental studies on a set of benchmark datasets demonstrate that the probabilistic model can achieve competitive performance compared with other multilabel learning algorithms.

Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers locally and exchanging encrypted information. This paper studies the vertical federated learning structure for logistic regression where the data sets at two parties have the same sample IDs but own disjoint subsets of features. Existing frameworks adopt the first-order stochastic gradient descent algorithm, which requires large number of communication rounds. To address the communication challenge, we propose a quasi-Newton method based vertical federated learning framework for logistic regression under the additively homomorphic encryption scheme. Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round. Numerical results demonstrate the advantages of our approach over the first-order method.

While many methods for learning vector space embeddings have been proposed in the field of Natural Language Processing, these methods typically do not distinguish between categories and individuals. Intuitively, if individuals are represented as vectors, we can think of categories as (soft) regions in the embedding space. Unfortunately, meaningful regions can be difficult to estimate, especially since we often have few examples of individuals that belong to a given category. To address this issue, we rely on the fact that different categories are often highly interdependent. In particular, categories often have conceptual neighbors, which are disjoint from but closely related to the given category (e.g.\ fruit and vegetable). Our hypothesis is that more accurate category representations can be learned by relying on the assumption that the regions representing such conceptual neighbors should be adjacent in the embedding space. We propose a simple method for identifying conceptual neighbors and then show that incorporating these conceptual neighbors indeed leads to more accurate region based representations.

Are you looking for Best Data Science Degree Online? This Online Data Science Course list will help you to become a top Data Scientist. Data science or data-driven science is one of today's fastest-growing fields. Do you want to become a Data Scientist in 2019? The list of the Data Science Degree will give you a clear idea from data science definition to expert's levels. If you don't know how to get data scientist certification then this data science certificate programs online will help you to get an online data science certificate. You will be able to get Microsoft data science certification or even Harvard data science certificate with this excellent collection of online courses. Also, this Data Science training will give you an idea about data science, python, data scientist, big data, analytics, machine learning, deep learning and Artificial Intelligence (AI) which are the most booming topics now. You can be a data science master in a short period of time. All big companies, publishers, advertisers, and other industries are now highly depended on data science or machine learning. So, it is high time to learn some skills in data science, for example, get the high demanded Data Science online certifications. How does it work at the present time, why data scientist's career and data science jobs are in top position? If you like a trendy career, you have that opportunity right now and get hired by the big industries. At the same time, online entrepreneurs and business personals also need to update themselves with the fundamental machine learning skills to compete with the fast-moving industry. Below are few best Data Science online courses that might assist you to jump-start the knowledge of data science sector. Best Data Science online tutorial and programs listing displays the'Best Course,' 'Product Description,' 'Rating,' 'Students Enrolled' 'Product's Image' and as well as an Enroll button to purchase the Courses from respective learning platforms for your convenience. Description: If you want to become a successful data scientist then you should take this best data science course. Just learning statistics, data visualization and data wrangling is not enough. You also need to know how to ask the right questions and tell the right story from your data. Description: This is an intermediate level data science course. Here you are going to learn to implement the advance data science concepts like inferential statistics and machine learning. The best data science certification promises you to get hired by a corporation after doing this course. As first you do the course then pay for it only if you get a data science job. So making an investment in your learning is completely risk-free now. You are going to master the foundational skills that are needed for you to do a job in the data science industry.

Sign in to report inappropriate content. Linear Regression in AI - video will help you understand the basics Linear Regression in a supervised learning algorithm, Types of linear regression.

Neural Architecture Search methods are effective but often use complex algorithms to come up with the best architecture. We propose an approach with three basic steps that is conceptually much simpler. First we train N random architectures to generate N (architecture, validation accuracy) pairs and use them to train a regression model that predicts accuracy based on the architecture. Next, we use this regression model to predict the validation accuracies of a large number of random architectures. Finally, we train the top-K predicted architectures and deploy the model with the best validation result. While this approach seems simple, it is more than 20 times as sample efficient as Regularized Evolution on the NASBench-101 benchmark and can compete on ImageNet with more complex approaches based on weight sharing, such as ProxylessNAS.

Model selection is the process of selecting one final machine learning model from among a collection of candidate machine learning models for a training dataset. Model selection is a process that can be applied both across different types of models (e.g.

We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network. It is noticed that neither a latent factor model nor a logistic regression model alone is sufficient to capture the structure of the data. The proposed model has a latent (i.e., factor analysis) model to represents the main technological trends (a.k.a., factors), and adds a sparse component that captures the remaining ad-hoc dependence. Parameter estimation is carried out through the construction of a joint-likelihood function of edges and properly chosen penalty terms. The convexity of the objective function allows us to develop an efficient algorithm, while the penalty terms push towards a low-dimensional latent component and a sparse graphical structure. Simulation results show that the proposed method works well in practical situations. The proposed method has been applied to a real application, which contains a citation network of statisticians (Ji and Jin, 2016). Some interesting findings are reported.