Sparsity learning with known grouping structures has received considerable attention due to wide modern applications in high-dimensional data analysis. Although advantages of using group information have been well-studied by shrinkage-based approaches, benefits of group sparsity have not been well-documented for greedy-type methods, which much limits our understanding and use of this important class of methods. In this paper, generalizing from a popular forward-backward greedy approach, we propose a new interactive greedy algorithm for group sparsity learning and prove that the proposed greedy-type algorithm attains the desired benefits of group sparsity under high dimensional settings. An estimation error bound refining other existing methods and a guarantee for group support recovery are also established simultaneously. In addition, an interactive feature is incorporated to allow extra algorithm flexibility without compromise in theoretical properties. The promising use of our proposal is demonstrated through numerical evaluations including a real industrial application in human activity recognition.

In this paper we establish fast convergence rate of stochastic variance reduction gradient (SVRG) for a class of problems motivated by applications in high dimensional statistics where the problems are not strongly convex, or even non-convex. High-dimensional statistics has achieved remarkable success in the last decade, including results on consistency and rates for various estimator under non-asymptotic high-dimensional scaling, especially when the problem dimensionp is larger than the number of datan [e.g., Negahban et al., 2009, Cand es and Recht, 2009, and many others [Candes et al., 2006, Wainwright, 2006, Chen et al., 2011]] . It is now well known that while this setup appears ill-posed, the estimation or recovery is indeed possible by exploiting the underlying structure of the parameter space - notable examples include sparse vectors, low-rank matrices, and structured regression functions, among others. Recently, estimators leading to non-convex optimizations have gained fast growing attention. Not only it typically has better statistical properties in the high dimensional regime, but also in contrast to common belief, under many cases there exist efficient algorithms that provably find near-optimal solutions Loh and Wainwright [2011], Zhang and Zhang [2012], Loh and Wainwright [2013] . Computation challenges of statistical estimators and machine learning algorithms have been an active area of study, thanks to countless applications involving big data - datasets where both p and n are large.

Gaussian process (GP) models have become a well-established frameworkfor the adaptive design of costly experiments, and notably of computerexperiments. GP-based sequential designs have been found practicallyefficient for various objectives, such as global optimization(estimating the global maximum or maximizer(s) of a function),reliability analysis (estimating a probability of failure) or theestimation of level sets and excursion sets. In this paper, we dealwith convergence properties of an important class of sequential designapproaches, known as stepwise uncertainty reduction (SUR) strategies.Our approach relies on the key observation that the sequence ofresidual uncertainty measures, in SUR strategies, is generally asupermartingale with respect to the filtration generated by theobservations. We study the existence of SUR strategies and establishgeneric convergence results for a broad class thereof. We alsointroduce a special class of uncertainty measures defined in terms ofregular loss functions, which makes it easier to check that ourconvergence results apply in particular cases. Applications of thelatter include proofs of convergence for the two main SUR strategiesproposed by Bect, Ginsbourger, Li, Picheny and Vazquez (Stat. Comp.,2012). To the best of our knowledge, these are the first convergenceproofs for GP-based sequential design algorithms dedicated to theestimation of excursions sets and their measure. Coming to globaloptimization algorithms, we also show that the knowledge gradientstrategy can be cast in the SUR framework with an uncertaintyfunctional stemming from a regular loss, resulting in furtherconvergence results. We finally establish a new proof of convergencefor the expected improvement algorithm, which is the first proof forthis algorithm that applies to any GP with continuous sample paths.

The statistical dependencies which independent component analysis (ICA) cannot remove often provide rich information beyond the linear independent components. It would thus be very useful to estimate the dependency structure from data. While such models have been proposed, they usually concentrated on higher-order correlations such as energy (square) correlations. Yet, linear correlations are a most fundamental and informative form of dependency in many real data sets. Linear correlations are usually completely removed by ICA and related methods, so they can only be analyzed by developing new methods which explicitly allow for linearly correlated components. In this paper, we propose a probabilistic model of linear non-Gaussian components which are allowed to have both linear and energy correlations. The precision matrix of the linear components is assumed to be randomly generated by a higher-order process and explicitly parametrized by a parameter matrix. The estimation of the parameter matrix is shown to be particularly simple because using score matching, the objective function is a quadratic form. Using simulations with artificial data, we demonstrate that the proposed method improves identifiability of non-Gaussian components by simultaneously learning their correlation structure. Applications on simulated complex cells with natural image input, as well as spectrograms of natural audio data show that the method finds new kinds of dependencies between the components.

The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting. To further investigate the properties and feasibility on real-world applications, this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a novel reinforcement learning (RL) benchmark that aims at being realistic by including a variety of aspects found in industrial applications, like continuous state and action spaces, a high dimensional, partially observable state space, delayed effects, and complex stochasticity. The experimental results of PSO-P on IB are compared to results of closed-form control policies derived from the model-based Recurrent Control Neural Network (RCNN) and the model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not only of interest for academic benchmarks, but also for real-world industrial applications, since it also yielded the best performing policy in our IB setting. Compared to other well established RL techniques, PSO-P produced outstanding results in performance and robustness, requiring only a relatively low amount of effort in finding adequate parameters or making complex design decisions.

Google's next iPhone killer, the Pixel XL has been revealed in a new series of renders. The images, created by @onleaks and MySmartPrice, show the handset's new look, and most notably, its lack of a headphone socket. Previous leaked have also claimed it may also have a hidden feature - squeezable edges. The images, created by @onleaks and MySmartPrice, show the handset's new look, and most notably, its lack of a headphone socket. 'The phones feature considerably less bezel compared to the Pixel and the Pixel XL, said mysmartprice.com

Facebook on Wednesday reported a surge in profits in the past quarter, fueled by strong growth in money-making ads to its more than two billion users. Net profit in the second quarter leapt 71 percent from a year ago to $3.9 billion while revenue climbed 45 percent to $9.3 billion. In after-hours trade, Facebook shares rose some two percent to $166.83 on the stronger-than-expected results. Recent reports suggest Facebook may be working on its own smart speaker to compete with Google Home and Amazon Echo in the budding market for home digital assistants. Facebook could also be working on a smartphone, according to paperwork recently spotted by cyber sleuths which the tech giant filed earlier this year.

Apple has already begun test manufacturing of the three new iPhones it will unveil in September, it has been claimed. Previous reports have said the firm may be forced to delay its eagerly anticipated iPhone 8 until later in they year. However, it now appears the firm is back on track, with the handset expected to be revealed in September. According to Twitter leaker Benjamin Geskin, all three new 2017 iPhone models have begun trial production, including the iPhone 7s, iPhone 7s Plus, and iPhone 8. According to Twitter leaker Benjamin Geskin, all three new 2017 iPhone models have begun trial production, including the iPhone 7s, iPhone 7s Plus, and iPhone 8.

Soft wearable robotic exosuits can help patients walk after strokes, a new study finds. Stroke is the leading cause of disability in the United States. More than 6.5 million Americans are stroke survivors, and the vast majority of them never fully recover the ability to walk. "The fact that many stroke survivors can't, say, walk to the store can in turn lead to a downward spiral when it comes to their health and quality of life," Conor Walsh, a soft roboticist at Harvard University. Recent breakthroughs in exoskeleton technology have shown promise as advances over canes, walkers, orthotics, and other traditional aides for stroke patients.

This team's solution write-up was originally published here by Daniel Hammack and cross-posted on No Free Hunch with their permission. Julian and I independently wrote summaries of our solution to the 2017 Data Science Bowl. What is below is my (Daniel's) summary. For the other half of the story, see Julian's post here. Julian is a freelance software/machine learning engineer so check out his site and work if you are looking to apply machine intelligence to your work. He won 3rd in last year's Data Science Bowl too! This blog post describes the story behind my contribution to the 2nd place solution to the 2017 Data Science Bowl. I will try to describe here why and when I did certain things but avoid the deep details on exactly how everything works. For those details see my technical report which has more of an academic flavor.