PhD thesis defense to be held on October 31, 2025, at 13:00 (Room 1.1.31, old Buildings of ECE NTUA)
Thesis title: Algorithmic Approaches for Efficiency and Explainability in Learning Problems
Abstract: This doctoral dissertation focuses on the development of supervised and unsupervised machine learning algorithms with an emphasis on two fundamental aspects – computational efficiency and explainability. The need for efficient and explainable models is particularly crucial in high-stakes domains such as healthcare, education and finance, where automated decisions can directly impact human lives. Within this context, the dissertation addresses three core problems. (1) In the label ranking problem, which generalizes multiclass classification, the task is modeled as a nonparametric regression problem. Interpretable algorithms based on decision trees and random forests are proposed, accompanied by new theoretical guarantees and extensions of existing statistical bounds to noisy and incomplete data. (2) For global counterfactual explanations, the problem is formulated as a multi-objective optimization balancing interpretability, cost, and effectiveness. Its computational hardness is formally established, and efficient clustering-based algorithms are introduced, achieving improved trade-offs across the competing criteria compared to existing approaches. (3) In the time series clustering problem, a two-phase algorithm is proposed that first applies Sparse Gaussian Process Regression to reduce the dimensionality of time series before clustering, significantly improving computational and memory efficiency while preserving clustering quality and enhancing interpretability. Overall, the dissertation makes both theoretical and practical contributions, providing new computational and statistical guarantees for fundamental learning problems and offering scalable, interpretable algorithms applicable to large-scale, real-world datasets in socially impactful domains.
Supervisor: Professor Dimitris Fotakis
PhD Student: Eleni Psaroudaki