We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a target concept to the entire classroom using O(min{d,N} log(1/eps)) examples, where d is the ambient dimension of the problem, N is the number of learners, and eps is the accuracy parameter. We show the robustness of our teaching strategy when the teacher has limited knowledge of the learners' internal dynamics as provided by a noisy oracle. Further, we study the trade-off between the learners' workload and the teacher's cost in teaching the target concept. Our experiments validate our theoretical results and suggest that appropriately partitioning the classroom into homogenous groups provides a balance between these two objectives.

Graph classification has recently received a lot of attention from various fields of machine learning e.g. kernel methods, sequential modeling or graph embedding. All these approaches offer promising results with different respective strengths and weaknesses. However, most of them rely on complex mathematics and require heavy computational power to achieve their best performance. We propose a simple and fast algorithm based on the spectral decomposition of graph Laplacian to perform graph classification and get a first reference score for a dataset. We show that this method obtains competitive results compared to state-of-the-art algorithms.

We theoretically and empirically explore the explainability benefits of adversarial learning in logistic regression models on structured datasets. In particular we focus on improved explainability due to significantly higher $\textit{feature-concentration}$ in adversarially-learned models: Compared to natural training, adversarial training tends to more efficiently shrink the weights of non-predictive and weakly-predictive features, while model performance on natural test data only degrades slightly (and even sometimes improves), compared to that of a naturally trained model. We provide theoretical insight into this phenomenon via an analysis of the expectation of the logistic model weight updates by an SGD-based adversarial learning algorithm, where examples are drawn from a random binary data-generation process. We empirically demonstrate the feature-pruning effect on a synthetic dataset, some datasets from the UCI repository, and real-world large-scale advertising response-prediction data-sets from MediaMath. In several of the MediaMath datasets there are 10s of millions of data points, and on the order of 100,000 sparse categorical features, and adversarial learning often results in model-size reduction by a factor of 20 or higher, and yet the model performance on natural test data (measured by AUC) is comparable to (and sometimes even better than) that of the naturally trained model. We also show that traditional $\ell_1$ regularization does not even come close to achieving this level of feature-concentration. We measure "feature concentration" using the Integrated Gradients-based feature-attribution method of Sundararajan et. al (2017), and derive a new closed-form expression for 1-layer networks, which substantially speeds up computation of aggregate feature attributions across a large dataset.

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data mining algorithms, and usually require large amount of label information (such as class labels or pair/triplet constraints) to achieve satisfactory performance. However, the label information may be insufficient in real-world applications due to the high-labeling cost, and DML may fail in this case. Transfer metric learning (TML) is able to mitigate this issue for DML in the domain of interest (target domain) by leveraging knowledge/information from other related domains (source domains). Although achieved a certain level of development, TML has limited success in various aspects such as selective transfer, theoretical understanding, handling complex data, big data and extreme cases. In this survey, we present a systematic review of the TML literature. In particular, we group TML into different categories according to different settings and metric transfer strategies, such as direct metric approximation, subspace approximation, distance approximation, and distribution approximation. A summarization and insightful discussion of the various TML approaches and their applications will be presented. Finally, we indicate some challenges and provide possible future directions.

Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty. There exist a considerable number of methods for performing inference in probabilistic graphical models; however, they can be computationally costly due to significant time burden and/or storage requirements; or they lack theoretical guarantees of convergence and accuracy when applied to large scale graphical models. To this end, we propose the Universal Marginaliser Importance Sampler (UM-IS) -- a hybrid inference scheme that combines the flexibility of a deep neural network trained on samples from the model and inherits the asymptotic guarantees of importance sampling. We show how combining samples drawn from the graphical model with an appropriate masking function allows us to train a single neural network to approximate any of the corresponding conditional marginal distributions, and thus amortise the cost of inference. We also show that the graph embeddings can be applied for tasks such as: clustering, classification and interpretation of relationships between the nodes. Finally, we benchmark the method on a large graph (>1000 nodes), showing that UM-IS outperforms sampling-based methods by a large margin while being computationally efficient.

There is not a huge difference in the RMSE value, but a plot for the predicted and actual values should provide a more clear understanding. The RMSE value is almost similar to the linear regression model and the plot shows the same pattern. Like linear regression, kNN also identified a drop in January 2018 since that has been the pattern for the past years. We can safely say that regression algorithms have not performed well on this dataset. Let's go ahead and look at some time series forecasting techniques to find out how they perform when faced with this stock prices prediction challenge. ARIMA is a very popular statistical method for time series forecasting. ARIMA models take into account the past values to predict the future values.

Hello World of Machine Learning is a video series to acquaint, enable and empower you to understand What, How and When of Machine Learning This is chapter - 2 of the series and in this chapter, we'll be exploring our first Machine Learning Algorithm called KNN (K Nearest Neighbor). This chapter will explain 1. What is KNN and how it works? Hope it helps you in learning something new.. enjoy! Please take a moment to Like! Subscribe!

Artificial Intelligence (AI) systems sometimes make errors and will make errors in the future, from time to time. These errors are usually unexpected, and can lead to dramatic consequences. Intensive development of AI and its practical applications makes the problem of errors more important. Total re-engineering of the systems can create new errors and is not always possible due to the resources involved. The important challenge is to develop fast methods to correct errors without damaging existing skills. We formulated the technical requirements to the 'ideal' correctors. Such correctors include binary classifiers, which separate the situations with high risk of errors from the situations where the AI systems work properly. Surprisingly, for essentially high-dimensional data such methods are possible: simple linear Fisher discriminant can separate the situations with errors from correctly solved tasks even for exponentially large samples. The paper presents the probabilistic basis for fast non-destructive correction of AI systems. A series of new stochastic separation theorems is proven. These theorems provide new instruments for fast non-iterative correction of errors of legacy AI systems. The new approaches become efficient in high-dimensions, for correction of high-dimensional systems in high-dimensional world (i.e. for processing of essentially high-dimensional data by large systems).

In e-commerce, content quality of the product catalog plays a key role in delivering a satisfactory experience to the customers. In particular, visual content such as product images influences customers' engagement and purchase decisions. With the rapid growth of e-commerce and the advent of artificial intelligence, traditional content management systems are giving way to automated scalable systems. In this paper, we present a machine learning driven visual content management system for extremely large e-commerce catalogs. For a given product, the system aggregates images from various suppliers, understands and analyzes them to produce a superior image set with optimal image count and quality, and arranges them in an order tailored to the demands of the customers. The system makes use of an array of technologies, ranging from deep learning to traditional computer vision, at different stages of analysis. In this paper, we outline how the system works and discuss the unique challenges related to applying machine learning techniques to real-world data from e-commerce domain. We emphasize how we tune state-of-the-art image classification techniques to develop solutions custom made for a massive, diverse, and constantly evolving product catalog. We also provide the details of how we measure the system's impact on various customer engagement metrics.

Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System (ITS), it is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been a concerned open scientific issue, owing to the constraint of urban road network topological structure and the law of dynamic change with time, namely spatial dependence and temporal dependence. In order to capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the graph convolutional network is used to learn the complex topological structure to capture the spatial dependence and the gated recurrent unit is used to learn the dynamic change of traffic flow to capture the temporal dependence. And then, the T-GCN model is employed to realize the traffic forecasting task based on urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio temporal correlation from traffic data and the prediction effects outperform state-of-art baselines on real-world traffic datasets.