ALEXANDER GÜNTHER(SOFTWARE ENGINEERING: DEPENDABILITY, PROF. LIGGESMEYER)
hosted by Ph.D. Program in CS @ TU KL
"RELIABLE UPPER BOUNDS FOR FAILURE OF MACHINE LEARNING COMPONENTS: A MODEL SPECIFIC AND MODEL AGNOSTIC APPROACH"
In the last couple of years, machine learning has been one of the most famous and celebrated areas in computer science. AI Methods achieved stunning and impressive results in a wide range of tasks and applications, even super-human performance. This raised the wish to also use this method in safety-critical areas, for example, medicine or autonomous driving. That raises the problem of verifying the safety and security of these models. But common techniques to verify software, like fault trees, Petri nets, equivalence class testing, state-based testing, and so on, are not applicable to machine learning implementations due to their black box nature. The goal of my thesis is to develop new methods and techniques to generate reliable statements about their safety. In the first year, I develop a statistical-based, upper-bound estimation for the failure on demand in the case of special binary classifiers. Furthermore, I am currently investigating the uncertainty quantification of the model risk. As a next step, I want to have a look at the deployment area inside and outside the system boundaries.
|Time:||Thursday, 15.06.2023, 09:00|
|Place:||Blechhammer Hotel-Restaurant in Kaiserslautern|