5 papers
SMLP: Symbolic Machine Learning Prover (User Manual)
Franz Brauße, Zurab Khasidashvili, Konstantin Korovin
SMLP: Symbolic Machine Learning Prover an open source tool for exploration and optimization of systems represented by machine learning models. SMLP uses symbolic reasoning for ML m…
SMLP: Symbolic Machine Learning Prover
Franz Brauße, Zurab Khasidashvili, Konstantin Korovin
Symbolic Machine Learning Prover (SMLP) is a tool and a library for system exploration based on data samples obtained by simulating or executing the system on a number of input vec…
Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques
Zurab Khasidashvili
We propose a root-causing procedure for accelerating system-level debug using rule-based techniques. We describe the procedure and how it provides high quality debug hints for redu…
Bayesian Optimisation with Formal Guarantees
Franz Brauße, Zurab Khasidashvili, Konstantin Korovin
Application domains of Bayesian optimization include optimizing black-box functions or very complex functions. The functions we are interested in describe complex real-world system…
Word-level Symbolic Trajectory Evaluation
Supratik Chakraborty, Zurab Khasidashvili, Carl-Johan H. Seger +4
Symbolic trajectory evaluation (STE) is a model checking technique that has been successfully used to verify industrial designs. Existing implementations of STE, however, reason at…