Such studies include The Cancer Genome Atlas (TCGA; Hoadley et al., 2018) with multi-platform genomic data for tumor samples, and Human Connectome Project (HCP; Van Essen et al., 2013) with multi-modal brain images of healthy adults, among many others (Crawford et al., 2016; Jensen et al., 2017). The use of multiple data types can allow us to enhance understanding the etiology of many complex diseases, such as cancers (Ciriello et al., 2015; Campbell et al., 2018) and neurodegenerative diseases (Weiner et al., 2013; Saeed et al., 2017). Researchers hence have became highly interested in studying the shared information and individual features across multi-type datasets through separating their common and distinctive variation structures (van der Kloet et al., 2016; Smilde et al., 2017; Li et al., 2018). Let Y k R p k n be the k -th row-mean centered dataset obtained on a common set of n objects for k 1,...,K, where p k is the number of variables for the k -th dataset. One popular approach for disentangling their common and distinctive variation structures is to decompose each data matrix into Y k X k E k C k D k E k for k 1,...,K, (1) where { X k} K k 1 are low-rank signal matrices with { E k} K k 1 being additive noise matrices, { C k} K k 1 are low-rank common-variation matrices that represent the signal data coming from the common mechanism shared across all datasets, and { D k} K k 1are low-rank distinctive-variation matrices each from the distinctive mechanism of each single dataset that is not shared by all.

We consider the problem of discriminatively learning restricted Boltzmann machines in the presence of relational data. Unlike previous approaches that employ a rule learner (for structure learning) and a weight learner (for parameter learning) sequentially, we develop a gradient-boosted approach that performs both simultaneously. Our approach learns a set of weak relational regression trees, whose paths from root to leaf are conjunctive clauses and represent the structure, and whose leaf values represent the parameters. When the learned relational regression trees are transformed into a lifted RBM, its hidden nodes are precisely the conjunctive clauses derived from the relational regression trees. This leads to a more interpretable and explainable model. Our empirical evaluations clearly demonstrate this aspect, while displaying no loss in effectiveness of the learned models.

Connecting consumers with relevant products is a very important problem in both online and offline commerce. In physical retail, product placement is an effective way to connect consumers with products. However, selecting product locations within a store can be a tedious process. Moreover, learning important spatial patterns in offline retail is challenging due to the scarcity of data and the high cost of exploration and experimentation in the physical world. To address these challenges, we propose a stochastic model of spatial demand in physical retail. We show that the proposed model is more predictive of demand than existing baselines. We also perform a preliminary study into different automation techniques and show that an optimal product allocation policy can be learned through Deep Q-Learning.

Maybe we are interested in knowing what's likely to happen in each game they play. In this case, knowing the total number of home runs hit over the course of the season isn't going to be quite as helpful – to make an accurate forecast about the next game, we need to have game-level data. Is the game being played at home or away? The answers to these questions are all crucial to generating an accurate prediction of the Giants' next game. This type of forecast is called a'bottom-up' or'rollup'-based forecast because predictions are made for each game based on the Giants' probability of winning each matchup.

Shiller's CAPE ratio is a popular and useful metric for measuring whether stock prices are overvalued or undervalued relative to earnings. Recently, Vanguard analysts Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz, and Joseph H. Davis have written a very interesting paper on forecasting equity returns using Shiller's CAPE and machine learning: "The Best of Both Worlds: Forecasting US Equity Market Returns using a Hybrid Machine Learning – Time Series Approach". First, what is the Shiller CAPE ratio? 1 If we do a simple regression of Shiller's CAPE ratio against future 10-year returns, we observe a very strong relationship. Here we see a historical chart of actual 10-year annualized stock returns vs. those predicted by Shiller's CAPE. Using the Shiller regression, the current CAPE of about 30 suggests near-zero real return over the next 10 years.

Shiller's CAPE ratio is a popular and useful metric for measuring whether stock prices are overvalued or undervalued relative to earnings. Recently, Vanguard analysts Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz, and Joseph H. Davis have written a very interesting paper on forecasting equity returns using Shiller's CAPE and machine learning: "The Best of Both Worlds: Forecasting US Equity Market Returns using a Hybrid Machine Learning – Time Series Approach". First, what is the Shiller CAPE ratio? 1 If we do a simple regression of Shiller's CAPE ratio against future 10-year returns, we observe a very strong relationship. Here we see a historical chart of actual 10-year annualized stock returns vs. those predicted by Shiller's CAPE. Using the Shiller regression, the current CAPE of about 30 suggests near-zero real return over the next 10 years.

Carbon capture and storage (CCS) can aid decarbonization of the atmosphere to limit further global temperature increases. A framework utilizing unsupervised learning is used to generate a range of subsurface geologic volumes to investigate potential sites for long-term storage of carbon dioxide. Generative adversarial networks are used to create geologic volumes, with a further neural network used to sample the posterior distribution of a trained Generator conditional to sparsely sampled physical measurements. These generative models are further conditioned to historic dynamic fluid flow data through Bayesian inversion to improve the resolution of the forecast of the storage capacity of injected carbon dioxide.

Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on a new estimating equation that adapts to censoring and leads to quantile score whenever the data do not exhibit censoring. The proposed procedure named {\it censored quantile regression forest}, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.

The tremendous boost in the next generation sequencing and in the omics technologies makes it possible to characterize human gut microbiome (the collective genomes of the microbial community that reside in our gastrointestinal tract). While some of these microorganisms are considered as essential regulators of our immune system, some others can cause several diseases such as Inflammatory Bowel Diseases (IBD), diabetes, and cancer. IBD, is a gut related disorder where the deviations from the healthy gut microbiome are considered to be associated with IBD. Although existing studies attempt to unveal the composition of the gut microbiome in relation to IBD diseases, a comprehensive picture is far from being complete. Due to the complexity of metagenomic studies, the applications of the state of the art machine learning techniques became popular to address a wide range of questions in the field of metagenomic data analysis. In this regard, using IBD associated metagenomics dataset, this study utilizes both supervised and unsupervised machine learning algorithms, i) to generate a classification model that aids IBD diagnosis, ii) to discover IBD associated biomarkers, iii) to find subgroups of IBD patients using k means and hierarchical clustering. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR) and Extreme Gradient Boosting (XGBoost). In our experiments with 10 fold cross validation, XGBoost had a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. We observed that compared to the single classifiers, ensemble methods such as kNN and logitboost resulted in better performance measures for the classification of IBD.

X, MONTH YEAR 1 Coupled Tensor Completion via Low-rank Tensor Ring Huyan Huang, Yipeng Liu, Senior Member, IEEE, Ce Zhu, Fellow, IEEE Abstract --The coupled tensor decomposition aims to reveal the latent data structure which may share common factors. Using the recently proposed tensor ring decomposition, in this paper we propose a non-convex method by alternately optimizing the latent factors. We provide an excess risk bound for the proposed alternating minimization model, which shows the improvement in completion performance. The proposed algorithm is validated on synthetic data. Index T erms--tensor ring, coupled tensor completion, alternating least squares, excess risk bound, permutational Rademacher complexity I. I NTRODUCTION Tensor is a multidimensional array and able to model the interaction between different modes in high-dimensional data.