Machine Learning Foundations
DESCRIPTION
We study the foundations of Machine Learning and Statistics. We study existing methods and models, and develop new ones, targeting challenging learning modalities. We develop techniques that address important challenges, including learning from data that are high-dimensional, contain biases, or are corrupted, and addressing fairness, incentive and reliability issues that arise at model deployment.
RESEARCHERS
Alexandros Potamianos
National Technical University of Athens
Alkmini Sgouritsa
Athens University of Economics and Business
Antonios Anastasopoulos
George Mason University
Archontis Giannakidis
Nottingham Trent University
Aris Pagourtzis
National Technical University of Athens
Christos H. Papadimitriou
Columbia University
Christos Tzamos
National and Kapodistrian University of Athens & University of Wisconsin–Madison
Dimitrios Kanoulas
University College London
Dimitris Fotakis
National Technical University of Athens
Efstratios Gavves
University of Amsterdam
George Korpas
HSBC Labs & Czech Technical University in Prague
Georgios Amanatidis
University of Essex
Georgios Christodoulou
Aristotle University of Thessaloniki
Giorgos Papanastasiou
University of Essex
Ioannis Mitliagkas
University of Montreal
Ioannis Panageas
University of California, Irvine
Konstantinos Tsakalidis
University of Liverpool
Sotirios Sabanis
University of Edinburgh & National Technical University of Athens
Themos Stafylakis
Athens University of Economics and Business
Vasileios Nakos
National and Kapodistrian University of Athens
Vasilis Gkatzelis
Drexel University
