Francesco Orabona
CAREER: Parameter-free Optimization Algorithms for Machine Learning
Machine Learning (ML) has been described as the fuel of the next industrial revolution. Yet, current state-of-the-art ML algorithms still heavily rely on having a human in the loop in order to work properly. Indeed, the training process of a ML algorithm requires significant human intervention through twisting and tuning of the many knobs of […]
Foundations of Data Science Institute
The Foundations of Data Science Institute (FODSI) brings together a large and diverse team of researchers and educators from UC Berkeley, MIT, Boston University, Bryn Mawr College, Harvard University, Howard University, and Northeastern University, with the aim of advancing the theoretical foundations for the field of data science. Data science has emerged as a central […]
AF: Small: Collaborative Research: New Representations for Learning Algorithms and Secure Computation
Recent success of machine learning is due in part to the availability of large datasets for training and testing purposes. However, the training process is computationally intensive and collected datasets are often privacy sensitive. This has led to providing Machine Learning as a Service (MLaaS), where data providers store their data in the cloud and […]
Collaborative Research: TRIPODS Institute for Optimization and Learning
This Phase I project forms an NSF TRIPODS Institute, based at Lehigh University and in collaboration with Stony Brook and Northwestern Universities, with a focus on new advances in tools for machine learning applications. A critical component for machine learning is mathematical optimization, where one uses historical data to train tools for making future predictions […]