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, Greece & HERON Robotics Center of Excellence, Greece
Alkmini Sgouritsa
Athens University of Economics and Business, Greece
Antonios Anastasopoulos
George Mason University, USA
Archontis Giannakidis
Nottingham Trent University, UK
Aris Pagourtzis
National Technical University of Athens, Greece
Christos H. Papadimitriou
Columbia University, USA
Christos Tzamos
National and Kapodistrian University of Athens, Greece & University of Wisconsin–Madison, USA
Dimitrios Kanoulas
University College London, UK
Dimitris Fotakis
National Technical University of Athens, Greece
Efstratios Gavves
University of Amsterdam, The Netherlands
George Korpas
HSBC Quantum Technologies Group, HSBC Global Services & Czech Technical University in Prague, Czech Republic
Georgios Amanatidis
Athens University of Economics and Business, Greece
Georgios Christodoulou
Aristotle University of Thessaloniki, Greece
Giorgos Papanastasiou
University of Essex, UK
Ioannis Mitliagkas
University of Montreal, Canada
Ioannis Panageas
University of California, Irvine, USA
Konstantinos Tsakalidis
University of Liverpool, UK
Sotirios Sabanis
University of Edinburgh, UK & National Technical University of Athens, Greece
Themos Stafylakis
Athens University of Economics and Business, Greece
Vasileios Nakos
National and Kapodistrian University of Athens, Greece & University of Wisconsin–Madison, USA
Vasilis Gkatzelis
Drexel University, USA
