UserReplay announced today the addition of a machine learning feature to its existing solution in order to better assist companies with gaining insight into their customers' online experiences and resolve issues in real time. UserReplay machine learning uncovers hard-to-discover revenue opportunities hidden in the powerful data set captured by UserReplay. The biggest benefit of UserReplay machine learning is its systematic and automatic uncovering of pain points that requires no human intervention. Analysts and consultants can now spend their time on more valuable endeavors while still gleaning comprehensive insights from mining customer experience data. For example, a leading national retailer's website was causing customers to see the error message "Sorry, some of the items in your basket just sold out." However, there was no indication of which item was sold out and none of the items when checked individually showed a stock warning.

Self-driving cars are raising hopes that we'll get a lot done when we don't have to drive anymore. According to a University of Michigan study, that's about as likely as a parent finishing two memos and a big presentation while taking a teen-age learner out to drive. The average U.S. driver spends an hour a day in their car, but the study concluded that for 62 percent of Americans, freeing up that driving time won't make them any more productive. And the findings suggest riding in a self-driving car may be a white-knuckle nightmare of nerves, car sickness, unsafe seats and flying gadgets. Maybe ordinary people have sensed this already.

The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high density separated from each other by zones of lower density, so that they are closely related to certain regions around the density modes. If the true density is indeed in the assumed class of mixture densities, then mixture model clustering allows to scrutinize more subtle situations than modal clustering. However, when mixture modeling is used in a nonparametric way, taking advantage of the denseness of the sieve of mixture densities to approximate any density, then the correspondence between clusters and mixture components may become questionable. In this paper we introduce two methods to adopt a modal clustering point of view after a mixture model fit. Numerous examples are provided to illustrate that mixture modeling can also be used for clustering in a nonparametric sense, as long as clusters are understood as the domains of attraction of the density modes.

Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data. Kernel and Multiple Kernel CCA are popular methods for finding nonlinear correlations between high-dimensional datasets. Data was gathered from 183 patients, 79 with schizophrenia and 104 healthy controls. Kernel and Multiple Kernel CCA represent new avenues for studying schizophrenia, because, to our knowledge, these methods have not been used on these data before. Classification is performed via k-means clustering on the kernel matrix outputs of the Kernel and Multiple Kernel CCA algorithm. Accuracies of the Kernel and Multiple Kernel CCA classification are compared to that of the regularized linear CCA algorithm classification, and are found to be significantly more accurate. Both algorithms demonstrate maximal accuracies when the combination of DNA methylation and fMRI data are used, and experience lower accuracies when the SNP data are incorporated.

We consider the estimation and inference of sparse graphical models that characterize the dependency structure of high-dimensional tensor-valued data. To facilitate the estimation of the precision matrix corresponding to each way of the tensor, we assume the data follow a tensor normal distribution whose covariance has a Kronecker product structure. A critical challenge in the estimation and inference of this model is the fact that its penalized maximum likelihood estimation involves minimizing a non-convex objective function. To address it, this paper makes two contributions: (i) In spite of the non-convexity of this estimation problem, we prove that an alternating minimization algorithm, which iteratively estimates each sparse precision matrix while fixing the others, attains an estimator with the optimal statistical rate of convergence. Notably, such an estimator achieves estimation consistency with only one tensor sample, which was not observed in the previous work. (ii) We propose a de-biased statistical inference procedure for testing hypotheses on the true support of the sparse precision matrices, and employ it for testing a growing number of hypothesis with false discovery rate (FDR) control. The asymptotic normality of our test statistic and the consistency of FDR control procedure are established. Our theoretical results are further backed up by thorough numerical studies. We implement the methods into a publicly available R package Tlasso.

Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in Bayesian computations. However, they need to access the full data set in order to evaluate the posterior density at every step of the algorithm. This results in a great computational burden in big data applications. In contrast to MCMC methods, Stochastic Gradient MCMC (SGMCMC) algorithms such as the Stochastic Gradient Langevin Dynamics (SGLD) only require access to a batch of the data set at every step. This drastically improves the computational performance and scales well to large data sets. However, the difficulty with SGMCMC algorithms comes from the sensitivity to its parameters which are notoriously difficult to tune. Moreover, the Root Mean Square Error (RMSE) scales as $\mathcal{O}(c^{-\frac{1}{3}})$ as opposed to standard MCMC $\mathcal{O}(c^{-\frac{1}{2}})$ where $c$ is the computational cost. We introduce a new class of Multilevel Stochastic Gradient Markov chain Monte Carlo algorithms that are able to mitigate the problem of tuning the step size and more importantly of recovering the $\mathcal{O}(c^{-\frac{1}{2}})$ convergence of standard Markov Chain Monte Carlo methods without the need to introduce Metropolis-Hasting steps. A further advantage of this new class of algorithms is that it can easily be parallelised over a heterogeneous computer architecture. We illustrate our methodology using Bayesian logistic regression and provide numerical evidence that for a prescribed relative RMSE the computational cost is sublinear in the number of data items.

If a given behavior of a multi-agent system restricts the phase variable to a invariant manifold, then we define a phase transition as change of physical characteristics such as speed, coordination, and structure. We define such a phase transition as splitting an underlying manifold into two sub-manifolds with distinct dimensionalities around the singularity where the phase transition physically exists. Here, we propose a method of detecting phase transitions and splitting the manifold into phase transitions free sub-manifolds. Therein, we utilize a relationship between curvature and singular value ratio of points sampled in a curve, and then extend the assertion into higher-dimensions using the shape operator. Then we attest that the same phase transition can also be approximated by singular value ratios computed locally over the data in a neighborhood on the manifold. We validate the phase transitions detection method using one particle simulation and three real world examples.

HERE is an increasing need to handle large multidimensional datasets that cannot efficiently be analyzed or processed using modern day computers. Due to the curse of dimensionality it is urgent to develop mathematical tools which can evaluate information beyond the properties of large matrices [1]. The essential goal is to reduce the dimensionality of multidimensional data, represented by tensors, with a minimal information loss by compressing the original tensor space to a lower-dimensional tensor space, also called the feature space [1]. Tensor decomposition is the most natural tool to enable such compressions [2]. Until recently, tensor compression is merely based on Tucker decomposition (TD) [3], also known as higher-order singular value decomposition (HOSVD) when orthogonality constraints on factor matrices are imposed [4].

This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state.

MRI scans can now be used to read emotions in the human brain, claim scientists. A new study shows that the brain-scanning technology can pinpoint specific emotions while a person is experiencing them. Researchers from Duke University claim to be able to'see' these emotions flickering across the brain. 'It's getting to be a bit like mind-reading,' said Kevin LaBar, a professor of psychology and neuroscience at Duke. 'Earlier studies have shown that functional MRI can identify whether a person is thinking about a face or a house. 'Our study is the first to show that specific emotions like fear and anger can be decoded from these scans as well.'