Gary R. Holt

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    Machine Learning: The High Interest Credit Card of Technical Debt
    Eugene Davydov
    Dietmar Ebner
    Vinay Chaudhary
    Michael Young
    SE4ML: Software Engineering for Machine Learning (NIPS 2014 Workshop)
    Preview abstract Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns. View details
    Ad Click Prediction: a View from the Trenches
    Michael Young
    Dietmar Ebner
    Julian Grady
    Lan Nie
    Eugene Davydov
    Sharat Chikkerur
    Dan Liu
    Arnar Mar Hrafnkelsson
    Tom Boulos
    Jeremy Kubica
    Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)(2013)
    Preview abstract Predicting ad click--through rates (CTR) is a massive-scale learning problem that is central to the multi-billion dollar online advertising industry. We present a selection of case studies and topics drawn from recent experiments in the setting of a deployed CTR prediction system. These include improvements in the context of traditional supervised learning based on an FTRL-Proximal online learning algorithm (which has excellent sparsity and convergence properties) and the use of per-coordinate learning rates. We also explore some of the challenges that arise in a real-world system that may appear at first to be outside the domain of traditional machine learning research. These include useful tricks for memory savings, methods for assessing and visualizing performance, practical methods for providing confidence estimates for predicted probabilities, calibration methods, and methods for automated management of features. Finally, we also detail several directions that did not turn out to be beneficial for us, despite promising results elsewhere in the literature. The goal of this paper is to highlight the close relationship between theoretical advances and practical engineering in this industrial setting, and to show the depth of challenges that appear when applying traditional machine learning methods in a complex dynamic system. View details
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