Acknowledgements This thesis marks the end of my PhD journey, a period that has been very rewarding but definitely not without challenges. Alongside the inherent difficulties of doing a PhD, I had to deal with a pandemic that forced us all to work from home. Worst of all, it was during this time that I had to suffer the loss of my father. Luckily, despite the long period of working from home and the at times solitary nature of a PhD project, I have not been alone throughout this journey. I have been fortunate enough to interact with many wonderful people, whose company I enjoyed a lot and who helped me grow both personally and professionally. For this, I want to express my sincere gratitude. First of all, I want to wholeheartedly thank my supervisors, for trusting me and giving me this opportunity, for their continued support, guidance, and wisdom. My first supervisor, dr. Vlado Menkovski, for countless brainstorm or feedback sessions, discussions, and interesting conversations, sometimes very to the point, other times more deviating or even philosophical. My second supervisor, dr. Mike Holenderski, for providing valuable feedback whenever needed, and for keeping me grounded in the here and now and focused on practical output. My promotor, prof.dr. Mykola Pechenizkiy, for support and encouragement throughout my PhD, and for leading the Data Mining group in a way that makes it a safe, pleasant, and inclusive environment for both quality research and deep social connections between colleagues. I am grateful to the committee members of my PhD defence, prof.dr. Jonathon Hare, prof.dr. Tommi Kärkkäinen, dr. Jakub Tomczak, and dr.habil. Cassio de Campos, for taking the time in their undoubtedly busy schedules to review my dissertation and provide insightful comments and suggestions. I want to thank my co-author and fellow PhD student Luis, for a very pleasant collaboration, and for persistence in navigating ourselves out of the “submission cycle of doom”, ultimately resulting in both of us travelling to Baltimore to
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