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

UC Berkeley

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