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Heterodox Methods for Interpretable and Efficient Artificial Intelligence

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Monday, 13. June (Pre-C. Day 1)
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In this workshop we will discuss (machine learning) architectures where the human involvement in the design of the model and its data ingestion process allows for both more energy efficient and more interpretable outcomes. Examples of such systems stretch from pure grammatical inference methods and probabilistic programming, where the model (family) is entirely constructed by human hands and only very specific model parameters are learned from data, to various types of interpretable neural network approaches where the specific workings of the output system is much less defined a priori. The goal is to spread knowledge about lesser known approaches to learning from data that use an increased level of human involvement, require less training data, and are tailored to achieve interpretable results in a more efficient way.
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