Machine Learning and Statistics: From Theory to Practice (25w5389)
Organizers
Muni Sreenivas Pydi (Université Paris Dauphine - PSL)
Sourish Das (Chennai Mathematical Institute)
Ambedkar Dukkipati (Indian Institute of Science)
Lalitha Sankar (Arizona State University)
Sunitha Sarwagi (IIT Bombay)
Varun Jog (University of Cambridge)
Description
The Chennai Mathematical Institute will host the "Machine Learning and Statistics: From Theory to Practice" workshop in Chennai, India from January 12 to January 17, 2025.
In recent years, the widespread adoption of machine learning (ML) algorithms across various domains has given rise to novel requirements. These include the imperative for ML algorithms to exhibit robustness in the face of data perturbations, uphold user privacy, and operate effectively within networked systems characterized by interdependencies.
To address these emerging needs, a new body of theoretical work has emerged, drawing inspiration from established disciplines such as information theory, high-dimensional statistics, and optimization. This workshop aims to convene researchers across diverse domains within machine learning who leverage rigorous statistical and mathematical methodologies to tackle these evolving challenges.
The Chennai Mathematical Institute (CMI) in Chennai, India, and the Banff International Research Station for Mathematical Innovation and Discovery (BIRS) in Banff, are collaborative Canada-US-Mexico ventures that provide an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station in Banff is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), and Alberta's Advanced Education