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Information Theory and Method of Types: Channels, Quantizers, and Divergences

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If you have a question about this talk, please contact Konstantina Palla.

Information Theory and Machine Learning share many concept, models, and inference methods, and in some cases offers complementary perspective on a problem. In this RCC we consider one of the latter case: quantization (IT) or feature extraction (ML) for classifier’s design.

We will start reviewing the original work of Shannon on noisy channel coding and some recent results on this topic. We will continue with its dual problem, rate distortion theory, and its implementation, the design of quantizers. We will consider the use of different divergences for quantizer’s design and we end up analysing its relationship with the loss function for learning the classifier.

This talk is part of the Machine Learning Reading Group @ CUED series.

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