The MLOPT research group is building the mathematical and computational foundations of machine learning and optimization theory. Our work aims to answer fundamental questions about
- the performance limits of machine learning systems
- tradeoffs between data size, computational complexity, and statistical accuracy
- the mathematical characterizations of functions and representations learned from data
- computational and communication complexity in distributed learning and optimization
- privacy and fairness in machine learning and data science
- the interpretability, scalability, and safety of algorithms