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On the Parameterized Complexity of Learning First-Order Logic
We analyse the complexity of learning first-order queries in a model-theoretic framework for supervised learning introduced by (Grohe and Tur\'an, TOCS 2004).
Logic in Computer Science
Learning Concepts Described by Weight Aggregation Logic
We consider weighted structures, which extend ordinary relational structures by assigning weights, i. e. elements from a particular group or ring, to tuples present in the structure.
Learning Concepts Definable in First-Order Logic with Counting
That is, we prove that, for structures of unbounded degree, learning is not possible in sublinear time, even for classifiers definable in plain first-order logic.
