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Workshop highlights the impact of regularization and identifiability on algorithms in machine learning, indicating future implications for data modeling.
In machine learning, a field addressing the extraction of information and structure from finite data with the means of computer science and mathematics, maps from finite-dimensional spaces of data or computations into spaces of higher, or infinite dimensionality are a central theme. The workshop brought together researchers with diverse viewpoints to discuss how different theoretical sub-communities within the field treat the resulting ill-posed operations, and what kind of features of algorithms and models can emerge as a result.
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