In scenarios such as natural disasters, seasonal agriculture, and other short-duration operations, a rapidly deployable, autonomous mobile sensing system that decides where to take sensor measurements can be more versatile and costeffective than installing stationary sensors. In this work, we are interested in formulating a solution for persistent sensing of real-world stochastic phenomena using a team of mobile robots, even when the underlying covariance structure changes sharply across time, such as sunlight variation in a forest understory (Figure 1). Assuming no prior knowledge on the underlying model of the phenomenon dynamics, this presents two challenges: 1) adapting a belief on the underlying model based on recently observed phenomenon dynamics and 2) correspondingly optimizing the next sensing locations. While exactly modeling stochastic real-world phenomena remains a significant challenge, this work deals mainly with modeling the underlying covariance structure. The underlying covariance structure directly corresponds to information metrics such as entropy, required for evaluating the informativeness or representativeness of sensor readings across a set of locations [9, 13, 30]. Gaussian processes (GP) have emerged as a favored choice for this specific modeling goal primarily because of their nonparametric nature [14, 20, 33, 42].

Every year, thousands of people receive consumer product related injuries. Research indicates that online customer reviews can be processed to autonomously identify product safety issues. Early identification of safety issues can lead to earlier recalls, and thus fewer injuries and deaths. A dataset of product reviews from Amazon.com was compiled, along with \emph{SaferProducts.gov} complaints and recall descriptions from the Consumer Product Safety Commission (CPSC) and European Commission Rapid Alert system. A system was built to clean the collected text and to extract relevant features. Dimensionality reduction was performed by computing feature relevance through a Random Forest and discarding features with low information gain. Various classifiers were analyzed, including Logistic Regression, SVMs, Na{\"i}ve-Bayes, Random Forests, and an Ensemble classifier. Experimentation with various features and classifier combinations resulted in a logistic regression model with 70.2\% precision in the top 50 reviews surfaced. This classifier outperforms all benchmarks set by related literature and consumer product safety professionals.

We consider the problem of inductive matrix completion under the assumption that many features are non-informative, which leads to row- and column-sparse structure of coefficient matrix. Under the additional assumption on the low rank of coefficient matrix we propose the matrix factorization framework with group-lasso regularization on parameter matrices. We suggest efficient optimization algorithm for the solution of the obtained problem. From theoretical point of view, we prove the oracle generalization bound on the expected error of matrix completion. Corresponding sample complexity bounds show the benefits of the proposed approach compared to competitors in the sparse problems. The experiments on synthetic and real-world datasets show the state-of-the-art efficiency of the proposed method.

Embeddings… A word that every data scientist has heard by now, but mostly in the context of NLP. So why do we even bother embedding stuff? As I see it, creating quality embeddings and feeding it into models, is the exact opposite of the famous say "Garbage in, garbage out" . When you feed low quality data into your models, you put the entire load of learning on your model, as it will have to learn all the necessary conclusions that could be derived from the data. On the contrary, when you use quality embeddings, you already put some knowledge in your data and thus make the task of learning the problem easier for your models.

Experts have made it quite clear that 2018 will be a bright year for artificial intelligence and machine learning. Some of them have also expressed their opinion that "Machine learning tends to have a Python flavor because it's more user-friendly than Java". When it comes to data science, Python's syntax is the closest to the mathematical syntax and, therefore, is the language that is most easily understood and learned by professions like mathematicians or economists. Here I will present my top 10 list of the most useful Python tools for both machine learning and data science applications. If you feel like deepening your knowledge in either field and you don't know where to start, this is the best place for you!

Apache Thrift The Thrift stack is a common class hierarchy implemented in each language that abstracts out the tricky details of protocol encoding and network communication 26. Chukwa A data collection system for monitoring large distributed systems; Provides flexible/powerful toolkit to display, monitor, and analyze results; Architecture: Agents - run on each machine and emit data; Collectors - receive data from the agent and write it to stable storage; MapReduce jobs - parsing and archiving the data; Hadoop Infrastructure Care Center - a web-portal style interface.

We tackle the problem of penalty selection of regularization on the basis of the minimum description length (MDL) principle. In particular, we consider that the design space of the penalty function is high-dimensional. In this situation, the luckiness-normalized-maximum-likelihood(LNML)-minimization approach is favorable, because LNML quantifies the goodness of regularized models with any forms of penalty functions in view of the minimum description length principle, and guides us to a good penalty function through the high-dimensional space. However, the minimization of LNML entails two major challenges: 1) the computation of the normalizing factor of LNML and 2) its minimization in high-dimensional spaces. In this paper, we present a novel regularization selection method (MDL-RS), in which a tight upper bound of LNML (uLNML) is minimized with local convergence guarantee. Our main contribution is the derivation of uLNML, which is a uniform-gap upper bound of LNML in an analytic expression. This solves the above challenges in an approximate manner because it allows us to accurately approximate LNML and then efficiently minimize it. The experimental results show that MDL-RS improves the generalization performance of regularized estimates specifically when the model has redundant parameters.

We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification. The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map. Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parameterised by the score matrices, must \textit{alone} be used for classification. Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values. Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter. Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets. When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset. We also demonstrate improved robustness against the fast gradient sign method of adversarial attack.

In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive settings while explicitly alleviating noise effects from outliers. Experimental results on various datasets drawn from the web, text and image domains demonstrate the advantages of SEANO over the state-of-the-art methods in semi-supervised classification under transductive as well as inductive settings. We also show that a subset of parameters in SEANO are interpretable as outlier scores and can significantly outperform baseline methods when applied for detecting network outliers. Finally, we present the use of SEANO in a challenging real-world setting - flood mapping of satellite images and show that it is able to outperform modern remote sensing algorithms for this task. 1 Introduction Many applications are modeled and analyzed as attributed networks, where vertices represent entities with attributes and edges express the interactions or relationships between entities. In many scenarios, one also has knowledge about the labels of some vertices in an attributed network. Such networks are referred to as partially labeled attributed networks (PLANs). While PLANs contain much richer information than plain networks, they are also more challenging to analyze.

In many signal processing and machine learning applications, datasets containing private information are held at different locations, requiring the development of distributed privacy-preserving algorithms. Tensor and matrix factorizations are key components of many processing pipelines. In the distributed setting, differentially private algorithms suffer because they introduce noise to guarantee privacy. This paper designs new and improved distributed and differentially private algorithms for two popular matrix and tensor factorization methods: principal component analysis (PCA) and orthogonal tensor decomposition (OTD). The new algorithms employ a correlated noise design scheme to alleviate the effects of noise and can achieve the same noise level as the centralized scenario. Experiments on synthetic and real data illustrate the regimes in which the correlated noise allows performance matching with the centralized setting, outperforming previous methods and demonstrating that meaningful utility is possible while guaranteeing differential privacy.