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
Chara Podimata
MIT
Christos H. Papadimitriou
Columbia University
Christos Tzamos
University of Athens and UW Madison
Constantinos Caramanis
University of Texas, Austin
Constantinos Daskalakis
MIT
Ioannis Panageas
University of California, Irvine
Katerina Sotiraki
UC Berkeley
Konstantinos Tsakalidis
University of Liverpool
Vasileios Nakos
National and Kapodistrian University of Athens
