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Lecture Notes in Computer Science
AbstractWe present the first machine learning approach to the termination analysis of probabilistic programs. Ranking supermartingales (RSMs) prove that probabilistic programs halt, in expectation, within a finite number of steps. While previously RSMs were directly synthesised from source code, our method learns them from sampled execution traces. We introduce the neural ranking supermartingale: we let a neural network fit an RSM over execution traces and then we verify it over the source codedoi:10.1007/978-3-030-81688-9_1 fatcat:srgow3ul6ncypi5hkzdiwstovq