Learning In Practice: Reasoning About Quantization
There is a mismatch between the standard theoretical analyses of statistical machine learning and how learning is used in practice. The foundational assumption supporting the theory is that we can represent features and models using real-valued parameters. In practice, however, we do not use real numbers at any point during training or deployment. Instead, we rely on discrete and finite quantizations of the reals, typically floating points. In this paper, we propose a framework for reasoning about learning under arbitrary quantizations. Using this formalization, we prove the convergence of quantization-aware versions of the Perceptron and Frank-Wolfe algorithms. Finally, we report the results of an extensive empirical study of the impact of quantization using a broad spectrum of datasets.
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