Organizers: Adam Klivans (University of Texas at Austin; chair), Guy Bresler (Massachusetts Institute of Technology), Anindya De (Northwestern University), Philippe Rigollet (MIT), Sébastien Bubeck (Microsoft Research)
Because of the uncertainty caused by COVID-19, it is still unclear if this workshop will take place in-person or online only. Even an in-person version will have significantly reduced capacity; in-person attendance is expected to be limited to long-term program participants. In any case, the workshop will be open to the public for online participation. Please register to receive the zoom webinar access details. This page will be updated as soon as we have more information.
Many learning and testing problems naturally occur in a high-dimensional setting, where it is important to obtain results that are dimension-free (or with only mild dimension-dependence). As a representative example, one can consider the problems of testing and learning juntas: functions of many variables that depend (either precisely or approximately) only on a small subset of the variables. The problem of testing juntas is relatively well understood, while for the problem of learning juntas, there is a large gap between information-theoretic lower bounds and algorithmic results. More recent work introduced some variants of junta testing of a more geometric flavor, where many basic questions remain open. The techniques developed for these problems so far have involved a mixture of algorithmic methods and tools from high-dimensional probability. The purpose of this workshop is to make progress on these problems by bringing learning theorists together with geometers and probabilists who have expertise in high-dimensional phenomena.
Further details about this workshop will be posted in due course. Enquiries may be sent to the organizers workshop-hd3 [at] lists.simons.berkeley.edu (at this address).