Pinchas Gutter goes out of his way to find me biscuits. In a sun-baked living room in his north London home, he opens a packet of Rich Tea, sits down and tells me about the Holocaust. Gutter was seven years old when the second world war broke out. He lived in the Warsaw ghetto for three and a half years, took part in its uprising, survived six Nazi concentration camps – including the Majdanek extermination camp – and lived through a death march across Germany to Theresienstadt in occupied Czechoslovakia. "Remembrance is the secret of redemption, while forgetting leads to exile," he says, quoting Baal Shem Tov, the founder of Hasidic Judaism.

There are standard workflows in a machine learning project that can be automated. In Python scikit-learn, Pipelines help to to clearly define and automate these workflows. In this post you will discover Pipelines in scikit-learn and how you can automate common machine learning workflows.

Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods have been proposed which seek to represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to represent the label assignments in a low-dimensional space. However, by doing so, these methods actually neglect the tail labels - labels that are infrequently assigned to instances. We propose an embedding-based method that non-linearly embeds the label vectors using an stochastic approach, thereby predicting the tail labels more accurately. Moreover, the proposed method have excellent mechanisms for handling missing labels, dealing with large-scale datasets, as well as exploiting unlabeled data. With the best of our knowledge, our proposed method is the first multi-label classifier that simultaneously addresses all of the mentioned challenges. Experiments on real-world datasets show that our method outperforms stateof-the-art multi-label classifiers by a large margin, in terms of prediction performance, as well as training time.

We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification costs depend on both recommended actions and customers. Our method transforms such a data set to a new set which can be used with standard interpretable multi-class classification algorithms. The transformation has the desirable property that minimizing the standard misclassification penalty in this new space is equivalent to minimizing the custom cost function.

It is commonplace to encounter nonstationary data, of which the underlying generating process may change over time or across domains. The nonstationarity presents both challenges and opportunities for causal discovery. In this paper we propose a principled framework to handle nonstationarity, and develop some methods to address three important questions. First, we propose an enhanced constraint-based method to detect variables whose local mechanisms are nonstationary and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine some causal directions by taking advantage of information carried by changing distributions. Third, we develop a method for visualizing the nonstationarity of causal modules. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.

A computer that uses the quirks of quantum physics to work on data should be capable of things far beyond any machine in use today. Governments and large tech companies have spent huge sums trying to prove out that idea. Yet quantum computers have sometimes seemed like one of those technologies that are always 20 years away. Recently some leading research groups have come to think they can see a path to shortening that time considerably. Yesterday Google and researchers from the University of the Basque Country in Bilbao, Spain, published results that could lead to a shortcut to the long-awaited first conclusive demonstration of the power of quantum computing. The new result is one of the first fruits of a plan Google's quantum researchers laid out when I visited their new lab last year.

I keep posting ads for postdocs with other people but this is actually to work with little old me! The Department of Electrical and Computer Engineering (ECE) at Rutgers University is seeking a dynamic and motivated Postdoctoral Fellow to work on developing distributed machine learning algorithms that work on complex neuroimaging data. This work is in collaboration with the Mind Research Network in Albuquerque, New Mexico under NIH Grant 1R01DA040487-01A1. Candidates with a Ph.D. in Electrical Engineering, Computer Science, Statistics or related areas with experience in one of The Fellow will receive valuable experience in translational research as well as career mentoring, opportunities to collaborate with others outside the project within the ECE Department, DIMACS, and other institutions.The initial appointment is for 1 year but can be renewed subject to approval. Salary and compensation will be commensurate with the standard NIH scale for postdocs.

Recently I outlined a short series of posts I'll be writing about how I landed a job in autonomous vehicles. My background is that I have a pretty solid foundation in software engineering, including an undergraduate degree in computer science. But most recently my programming has been on the web, not so much in the machine learning and embedded systems areas that dominate vehicle software. Artificial Intelligence for Robotics (Udacity): This is a terrific and super-fun introduction into self-driving cars by Sebastian Thrun. Thrun is both the founder of Udacity and also the founder of Google's self-driving car project and also a former professor at Stanford. Taking the class is like being in the presence of greatness.

In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern classification and machine learning tasks, and various other data analyses. In this section want to recommend a way for installing the required Python-packages packages if you have not done so, yet. Otherwise you can skip this part. Although they can be installed step-by-step "manually", but I highly recommend you to take a look at the Anaconda Python distribution for scientific computing. Anaconda is distributed by Continuum Analytics, but it is completely free and includes more than 195 packages for science and data analysis as of today.

Machine learning is a hot topic, but what applications is the technology driving and what drives machine learning? The short answer, concludes a new research report on machine learning, is data--lots of data. "Data is available in volumes never dreamed of, which means the algorithms have more to get their teeth into," concluded a survey by market watcher 451 Research, noting that "machine learning lives or dies depending on the quantity and quality of the data available." Moreover, the rise of application containers in software development "means small [machine language] applications can be placed into software without breaking the rest of the application," the report's authors continue. With enterprises large and small going digital and computing resources ubiquitous, there's plenty of data available with which to train rather than simply program machines.