Computing the similarity between two data points plays a vital role in many machine learning algorithms. Metric learning has the aim of learning a good metric automatically from data. Most existing studies on metric learning for tree-structured data have adopted the approach of learning the tree edit distance. However, the edit distance is not amenable for big data analysis because it incurs high computation cost. In this paper, we propose a new metric learning approach for tree-structured data with pq-grams. The pq-gram distance is a distance for ordered labeled trees, and has much lower computation cost than the tree edit distance. In order to perform metric learning based on pq-grams, we propose a new differentiable parameterized distance, weighted pq-gram distance. We also propose a way to learn the proposed distance based on Large Margin Nearest Neighbors (LMNN), which is a well-studied and practical metric learning scheme. We formulate the metric learning problem as an optimization problem and use the gradient descent technique to perform metric learning. We empirically show that the proposed approach not only achieves competitive results with the state-of-the-art edit distance-based methods in various classification problems, but also solves the classification problems much more rapidly than the edit distance-based methods.

Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowledge graphs available, we can use this additional information to predict the time series better. Recently, there has been a focus on the application of deep representation learning on dynamic graphs. These methods predict the structure of the graph by reasoning over the interactions in the graph at previous time steps. In this paper, we propose a new framework to incorporate the information from dynamic knowledge graphs for time series prediction. We show that if the information contained in the graph and the time series data are closely related, then this inter-dependence can be used to predict the time series with improved accuracy. Our framework, DArtNet, learns a static embedding for every node in the graph as well as a dynamic embedding which is dependent on the dynamic attribute value (time-series). Then it captures the information from the neighborhood by taking a relation specific mean and encodes the history information using RNN. We jointly train the model link prediction and attribute prediction. We evaluate our method on five specially curated datasets for this problem and show a consistent improvement in time series prediction results.

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved. In this project, we will use a standard imbalanced machine learning dataset referred to as the "German Credit" dataset or simply "German."

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved. In this project, we will use a standard imbalanced machine learning dataset referred to as the "German Credit" dataset or simply "German."

In the supervised machine learning world, there are two types of algorithmic task often performed. One is called regression (predicting continuous values) and the other is called classification (predicting discrete values). In this blog, I have presented an example of a binary classification algorithm called "Binary Logistic Regression" which comes under the Binomial family with a logit link function. Binary logistic regression is used for predicting binary classes. For example, in cases where you want to predict yes/no, win/loss, negative/positive, True/False and so on.

Few years ago, I shared first machine learning story about insurance claim prediction. It's based on python code with logistic regression algorithm to build simple classification model as demonstration purpose. In 2020, it should be the year of Automatic Machine Learning (Auto ML) to make machine learning process clean, simple, fast and everyone can taste it, even peoples haven't knowledge in machine learning or data science. Recently, due to job related, I'm helping my customer to explore/evaluate data science and machine learning platform solution. That's surprise me that Azure Machine Learning (AML) is enhanced a lot and really provided an end-to-end solution platform and take care wide ranges of end users, from newbie to expert.

It is predated by three publications only: the seminal work of McCulloch and Pitts (1943) that hypothesized how neurons might work by relying on analogy to electrical circuits; Donald Hebb's book The Organization of Behavior (1949), which famously stipulated the basic principle of association of neurons by means of neural co-activation (i.e., assembling); and Frank Rosenblatt's work on the Perceptron (Rosenblatt, 1958). Importantly, however, the Widrow-Hoff rule was the first one that was successfully applied to real-life problems (e.g., noise cancellation in telephone lines which is used to date; cf., Haykin, 1999). After the initial excitement and until the (more) recent successes, models such as those mentioned above that were inspired biologically or, more specifically, neurally were ignored in favour of machines implementing von Neumann's traditional architecture. During the 1970s, the Widrow-Hoff rule was accidentally rediscovered in Psychology by Rescorla and Wagner (1972) who worked on animal and human learning, and by Kohonen (1972) in his work on Self-Organizing Maps in Computer Science. Finally, the widely known and successful Connectionist Parallel-Distributed Processing Models have the Widrow-Hoff rule as their principal building block (cf., McClelland & Rumelhart, 1986).

We develop an effective generation of adversarial attacks on neural models that output a sequence of probability distributions rather than a sequence of single values. This setting includes the recently proposed deep probabilistic autoregressive forecasting models that estimate the probability distribution of a time series given its past and achieve state-of-the-art results in a diverse set of application domains. The key technical challenge we address is effectively differentiating through the Monte-Carlo estimation of statistics of the joint distribution of the output sequence. Additionally, we extend prior work on probabilistic forecasting to the Bayesian setting which allows conditioning on future observations, instead of only on past observations. We demonstrate that our approach can successfully generate attacks with small input perturbations in two challenging tasks where robust decision making is crucial: stock market trading and prediction of electricity consumption.

Distributed machine learning algorithms play a significant role in processing massive data sets over large networks. However, the increasing reliance on machine learning on information and communication technologies (ICTs) makes it inherently vulnerable to cyber threats. This work aims to develop secure distributed algorithms to protect the learning from data poisoning and network attacks. We establish a game-theoretic framework to capture the conflicting goals of a learner who uses distributed support vector machines (SVMs) and an attacker who is capable of modifying training data and labels. We develop a fully distributed and iterative algorithm to capture real-time reactions of the learner at each node to adversarial behaviors. The numerical results show that distributed SVM is prone to fail in different types of attacks, and their impact has a strong dependence on the network structure and attack capabilities.

Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. This work makes algorithmic contributions to both challenges. First, to generate a realistic, diverse set of opponents, we develop a novel method for self-play based on replica-exchange Markov chain Monte Carlo. Second, we propose a distributionally robust bandit optimization procedure that adaptively adjusts risk aversion relative to uncertainty in beliefs about opponents' behaviors. We rigorously quantify the tradeoffs in performance and robustness when approximating these computations in real-time motion-planning, and we demonstrate our methods experimentally on autonomous vehicles that achieve scaled speeds comparable to Formula One racecars.