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 COVID-19, we cannot schedule in-person events on the Berkeley campus through December 2020. This workshop will take place online. It will be open to the public for online participation. Please register to receive the zoom webinar access details. Registration will open in early October.
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.
If you require accommodation for communication, please contact our Access Coordinator at simonsevents [at] berkeley.edu with as much advance notice as possible.
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).