@InProceedings{SAML21,
      AUTHOR = {Casimiro, Maria and Romano, Paolo and Garlan, David and Moreno, Gabriel A. and Kang, Eunsuk and Klein, Mark},
      TITLE = {Self-Adaptation for Machine Learning Based Systems},
      YEAR = {2021},
      MONTH = {14 September},
      BOOKTITLE = {Proceedings of the 1st International Workshop on Software Architecture and Machine Learning (SAML)},
      SERIES = {LNCS},
      ADDRESS = {Virtual, (Originally V\"axjö, Sweden)},
      PUBLISHER = {Springer},
      PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/SAML2021-paper6SAML21.pdf},
      ABSTRACT = {Today’s world is witnessing a shift from human-written software to machine-learned software, with the rise of systems that rely on machine learning. These systems typically operate in non-static environments, which are prone to unexpected changes, as is the case of self-driving cars and enterprise systems. In this context, machine-learned software can misbehave. Thus, it is paramount that these systems are capable of detecting problems with their machined-learned components and adapt themselves to maintain desired qualities. For instance, a fraud detection system that cannot adapt its machine-learned model to efficiently cope with emerging fraud patterns or changes in the volume of transactions is subject to losses of millions of dollars. In this paper, we take a first step towards the development of a framework aimed to self-adapt systems that rely on machine-learned components. We describe: (i) a set of causes of machine-learned component misbehavior and a set of adaptation tactics inspired by the literature on machine learning, motivating them with the aid of a running example; (ii) the required changes to the MAPE-K loop, a popular control loop for self-adaptive systems; and (iii) the challenges associated with developing this framework. We conclude the paper with a set of research questions to guide future work.},
      KEYWORDS = {Machine Learning, Self-adaptation}
}