Assumptions & Expectations in Semi-Supervised Machine Learning

The goal of this thesis is to investigate theoretical results in the field of semi-supervised learning, while also linking them to problems in related subjects as class probability estimation.<br/>

Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission data

Composite structures are highly valued for their strength-to-weight ratio, durability, and versatility, making them ideal for a variety of applications, including aerospace, automotive, and infrastructure. However, potential damage scenarios like impact, fatigue, and corrosion can lead to premature failure and pose a threat to safety. This...

DALLMi: Domain Adaption for LLM-based Multi-label Classifier

Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering domain shifts, e.g., classifier of movie reviews from IMDb to Rotten Tomatoes, adapting such an LLM-based multi-label classifier is challenging due to incomplete...