The Interface Between Selective Inference and Machine Learning (20w5214)

The Banff International Research Station will host the "The Interface Between Selective Inference and Machine Learning" workshop in Banff from March 8 to March 13, 2020.

Modern scientific data sets are large and complex, and often collected without a specific research question in mind. Rather, the express goal is to explore the data in search of novel insights, often using sophisticated algorithms to discover complex relationships and structure, and draw conclusions about what we find. Although data-driven exploration plays a vital and growing role in scientific discovery, standard statistical methods are invalidated when we use the data to decide what questions to ask. As advances in machine-learning algorithms for discovering patterns in data outpace our ability to provide reliable inferences about the patterns they find, the need for new selective inference methods -- statistically valid methods for answering questions suggested by the data -- has never been more urgent.

Recent years have seen a remarkable burst of exciting new methodologies and application areas at the interface of selective inference and machine learning. Our proposed workshop will bring together core researchers in each community, to share recent advances, generate new ideas, and to identify the most pressing problems in the field.

The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides 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 is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).