In this work we propose a data-driven modelization approach for the management of advertising investments of a firm. First, we propose an application of dynamic linear models to the prediction of an economic variable, such as global sales, which can use information from the environment and the investment levels of the company in different channels. After we build a robust and precise model, we propose a metric of risk, which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue. The advertising industry represents an estimate of US$ 529.43 billion [4] and this quantity is likely to increase in the following years. In parallel, there has been a recent interest in the application of data-driven models in the context of forecasting and decision making in the marketing industry.

The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. In this sense, cluster analysis algorithms are a key element of exploratory data analysis, due to their easiness in the implementation and relatively low computational cost. Among these algorithms, the K -means algorithm stands out as the most popular approach, besides its high dependency on the initial conditions, as well as to the fact that it might not scale well on massive datasets. In this article, we propose a recursive and parallel approximation to the K -means algorithm that scales well on both the number of instances and dimensionality of the problem, without affecting the quality of the approximation. In order to achieve this, instead of analyzing the entire dataset, we work on small weighted sets of points that mostly intend to extract information from those regions where it is harder to determine the correct cluster assignment of the original instances. In addition to different theoretical properties, which deduce the reasoning behind the algorithm, experimental results indicate that our method outperforms the state-of-the-art in terms of the trade-off between number of distance computations and the quality of the solution obtained.

Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian Processes (GP) that have proven to work effectively on a multiple supervised regression tasks. They combine the well calibrated uncertainty estimates of GPs with the great flexibility of multilayer models. In DGPs, given the inputs, the outputs of the layers are Gaussian distributions parameterized by their means and covariances. These layers are realized as Sparse GPs where the training data is approximated using a small set of pseudo points. In this work, we show that the computational cost of DGPs can be reduced with no loss in performance by using a separate, smaller set of pseudo points when calculating the layerwise variance while using a larger set of pseudo points when calculating the layerwise mean. This enabled us to train larger models that have lower cost and better predictive performance.

This article describes Team Kernel Glitches' solution to the National Institute of Justice's (NIJ) Real-Time Crime Forecasting Challenge. The goal of the NIJ Real-Time Crime Forecasting Competition was to maximize two different crime hotspot scoring metrics for calls-for-service to the Portland Police Bureau (PPB) in Portland, Oregon during the period from March 1, 2017 to May 31, 2017. Our solution to the challenge is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. Our model can be understood as an approximation to the popular log-Gaussian Cox Process model: we discretize the spatiotemporal point pattern and learn a log intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags, bandwidths for smoothing kernels, as well as cell shape, size, and rotation, were learned using crossvalidation. Resulting predictions exceeded baseline KDE estimates by 0.157. Performance improvement over baseline predictions were particularly large for sparse crimes over short forecasting horizons.

Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms. We take the principled view of distributionally robust optimization, which guarantees performance under adversarial input perturbations. By considering a Lagrangian penalty formulation of perturbation of the underlying data distribution in a Wasserstein ball, we provide a training procedure that augments model parameter updates with worst-case perturbations of training data. For smooth losses, our procedure provably achieves moderate levels of robustness with little computational or statistical cost relative to empirical risk minimization. Furthermore, our statistical guarantees allow us to efficiently certify robustness for the population loss. For imperceptible perturbations, our method matches or outperforms heuristic approaches.

With the emergence of connected devices (e.g., smartphones and smartmeters), pervasive systems generate growing amounts of digital traces as users undergo their everyday activities. These traces are crucial to service providers to understand their customers, to increase the degree of personalization, and enhance the quality of their services. For instance, personal digital traces stemming from public transit smartcards help transportation providers understand the commuting patterns of users; the usage statistics of home appliances can be used to improve energy efficiency; on-street cameras provide police officers with new ways of investigating crimes; content generated through mobile and wearables (such as posts in online social media or GPS running routes in specialized websites such as those for fitness) can be used to provide tailored content to individuals; bank transaction logs can be used to spot unusual activity in accounts. However, sharing these digital traces generated by pervasive systems with service providers might raise concerns with regards to privacy. Indeed, the processing and analysis of these digital traces can surface latent information about the behavior of the users. While service providers have to store the usergenerated data in large databases that guarantee a certain level of privacy (e.g., from storing the traces in an anonymized manner using randomly-generated identifiers instead of the real user's name and surname to using more sophisticated privacy-preserving techniques such as differential privacy), third parties such as advertisers that have access to the traces can leverage machine learning techniques to reveal personal information about the users and expose their privacy [1]. This includes inferring personal information about users and identifying a single individual from a collection of user-generated traces. Moreover, these traces might reveal information about the significant places routinely visited by the user, enabling the service provider to infer a wide range of personal information, including the user's place of residence and work and their future locations. To a further extent, presence traces can also be used to identify a specific individual in a population.

In this post, we will learn about Principal Component Analysis (PCA) -- a popular dimensionality reduction technique in Machine Learning. Our goal is to form an intuitive understanding of PCA without going into all the mathematical details. At the time of writing this post, the population of the United States is roughly 325 million. You may think millions of people will have a million different ideas, opinions, and thoughts, after all, every person is unique. Let's say you select 20 top political questions in the United States and ask millions of people to answer these questions using a yes or a no.

For a given software bug report, identifying an appropriate developer who could potentially fix the bug is the primary task of a bug triaging process. A bug title (summary) and a detailed description is present in most of the bug tracking systems. Automatic bug triaging algorithm can be formulated as a classification problem, which takes the bug title and description as the input, mapping it to one of the available developers (class labels). The major challenge is that the bug description usually contains a combination of free unstructured text, code snippets, and stack trace making the input data highly noisy. In the past decade, there has been a considerable amount of research in representing a bug report using tf-idf based bag-of-words feature (BOW) model. However, BOW model do not consider the syntactical and sequential word information available in the descriptive sentences.

The recent tax reform bill passed in the US has raised a lot of questions about wealth distribution in the country. While there's been a lot of focus on how the tax plan will impact income, there's been less attention focused on how this plan impacts the assets of wealthy households. The goal of this post is to show how the R programming language can be used to data mine publicly available sources to better understand the net worth of affluent households in the US. To answer these questions, we present descriptive statistics of this survey data and perform cluster analysis on affluent households, which we identify as households with a net worth of more than $1,000,000 USD. Based on the survey data, our analysis shows that the net worth of the top 1% of households in the US is $10.4M and the net worth of the top 0.1% of households is $43.2M.

SkySpark automatically analyzes building, energy and equipment data to identify issues, faults and opportunities for improved performance and operational savings. The new Machine Learning Tools provide support for supervised learning for prediction and forecasting through regression-based approaches, and classification using Support Vector Machine (SVM) techniques. "We have been working with select customers for almost a year as they tested our new machine learning tools. Their feedback helped us optimize the tools to make application as easy as possible. The results have been excellent and we are excited to announce their availability to the entire market. These tools further extend the capabilities of SkySpark and the range of solutions our partners are delivering to customers around the globe," said John Petze, Principal at SkyFoundry.