In our case, the data was provided by Safecity India, which is a platform launched on 2012, that crowdsources personal stories of sexual harassment and abuse in public spaces [2]. They have collected over 10,000 stories from over 50 cities in India, Kenya, Cameroon, and Nepal. More specifically they provided us a .cvs Additionally to the focal tasks of this project and as part of the NLP channel we decided to automate the category classification based on the sexual harassment case descriptions. Performing this classification task manually is time-consuming and leaving it entirely on the hands of the victim could produce ambiguity in the discrimination of the categories.

Whether on-premise or in the cloud, your data provides a link to the past and a glimpse into the future. Why did you lose past customers? Which current customers should you pay more attention to? Where are your new customers going to come from? In this blog, I'll talk about the capabilities of Teradata Vantage and its Machine Learning Engine, and how it can help you turn 100% of your data into answers that your business can use to pave a path to the future.

I have already (partially) reviewed previous studies where data have been proved to help identify signals that are relevant to assess the success potential of a startup. Even though the list is quite comprehensive, every study usually tends to look at one single factor and a couple of different success scenarios (namely, acquisition and IPO). In our work, we tried to have a more holistic view and use over 120,000 companies to spot signals not only for acquisitions and IPOs but also to compute the probability of raising a subsequent round of funding or shutting the startup down. In the same fashion as backtesting, we created a time-aware approach and analyzed companies that were no older than four years old by 2015 and tried to predict their success in the following three years. We also used more than a hundred variables as possible explanatory indicators of success, as well as five different models: Support Vector Machines (SVM); Decision Trees (DT); Random Forests (RF); Extremely Randomized Trees (ERT); and Gradient Tree Boosting (GTB).

Feature selection seeks a curated subset of available features such that they contain sufficient discriminative information for a given learning task. Online streaming feature selection (OSFS) further extends this to the streaming scenario where the model gets only a single pass at features, one at a time. While this problem setting allows for training high performance models with low computational and storage requirements, this setting also makes the assumption that there is a fixed number of samples, which is often invalidated in many real-world problems. In this paper, we consider a new setting called Online Streaming Feature Selection with Streaming Samples (OSFS-SS) with a fixed class label space, where both the features and the samples are simultaneously streamed. We extend the state-of-the-art OSFS method to work in this setting. Furthermore, we introduce a novel algorithm, that has applications in both the OSFS and OSFS-SS settings, called Geometric Online Adaptation (GOA) which uses a graph-based class conditional geometric dependency (CGD) criterion to measure feature relevance and maintain a minimal feature subset with relatively high classification performance. We evaluate the proposed GOA algorithm on both simulation and real world datasets highlighting how in both the OSFS and OSFS-SS settings it achieves higher performance while maintaining smaller feature subsets than relevant baselines.

Gradient descent and its variants are widely used in machine learning. However, oracle access of gradient may not be available in many applications, limiting the dire ct use of gradient descent. This paper proposes a method of estimating gradient to perform gradient descent, that converges to a stationary point for general non-convex optimization problems. Beyond the first-order stati onary properties, the second-order stationary properties are important in machine learning applications to achieve b etter performance. Gradient descent and its variants (e.g., Stochastic Gradie nt Descent) are widely used in machine learning due to their favorable computational properties, for examp le, in optimizing weights of a deep neural network. Recently, second order stationary guarant ees have been studied by using a perturbed version of gradient de scent [2].

Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records. Existing techniques for training differentially private (DP) models give rigorous privacy guarantees, but applying these techniques to neural networks can severely degrade model performance. This performance reduction is an obstacle to deploying private models in the real world. In this work, we improve the performance of DP models by fine-tuning them through active learning on public data. We introduce two new techniques - DIVERSEPUBLIC and NEARPRIVATE - for doing this fine-tuning in a privacy-aware way. For the MNIST and SVHN datasets, these techniques improve state-of-the-art accuracy for DP models while retaining privacy guarantees.

Recent advances in many enabling technologies such as sensing, computing and communication are instrumental in achieving this objective. Real-time health monitoring enables transitioning from traditional fixed schedule preventive maintenance to predictive maintenance, where decisions regarding maintenance are based on an objective assessment of the equipment health. The reduction in price of sensors has enabled widespread adoption of sensor technology for health monitoring. In 2004 the average cost of sensors was $1.30 and in the year 2020, it is expected to come down to $0.38 [1]. Industries such as mining, transportation, and aerospace are among the leaders in adoption of sensor-enabled predictive maintenance.

Concept drift in learning and classification occurs when the statistical properties of either the data features or target change over time; evidence of drift has appeared in search data, medical research, malware, web data, and video. Drift adaptation has not yet been addressed in high dimensional, noisy, low-context data such as streaming text, video, or images due to the unique challenges these domains present. We present a two-fold approach to deal with concept drift in these domains: a density-based clustering approach to deal with virtual concept drift (change in statistical properties of features) and a weak-supervision step to deal with real concept drift (change in statistical properties of target). Our density-based clustering avoids problems posed by the curse of dimensionality to create an evolving 'map' of the live data space, thereby addressing virtual drift in features. Our weak-supervision step leverages high-confidence labels (oracle or heuristic labels) to generate weighted training sets to generalize and update existing deep learners to adapt to changing decision boundaries (real drift) and create new deep learners for unseen regions of the data space. Our results show that our two-fold approach performs well with >90% precision in 2018, four years after initial deployment in 2014, without any human intervention.

In this work, we propose a simple model that provides permutation invariant maximally predictive prototype generator from a given dataset, which leads to interpretability of the solution and concrete insights to the nature and the solution of a problem. Our aim is to find out prototypes in the feature space to map the collection of instances (i.e. bags) to a distance feature space and simultaneously learn a linear classifier for multiple instance learning (MIL). Our experiments on classical MIL benchmark datasets demonstrate that proposed framework is an accurate and efficient classifier compared to the existing approaches.

By exploiting mini-batch stochastic gradient optimisation, variational inference has had great success in scaling up approximate Bayesian inference to big data. To date, however, this strategy has only been applicable to models of independent data. Here we extend mini-batch variational methods to state space models of time series data. To do so we introduce a novel generative model as our variational approximation, a local inverse autoregressive flow. This allows a subsequence to be sampled without sampling the entire distribution. Hence we can perform training iterations using short portions of the time series at low computational cost. We illustrate our method on AR(1), Lotka-Volterra and FitzHugh-Nagumo models, achieving accurate parameter estimation in a short time.