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Activity Number: 272 - Statistical Learning for Complex and High-Dimensional Data
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #300477
Title: Sparse Grid Meets Random Hashing: Learning High-Dimensional Functions of Few Variables
Author(s): Ming Yuan*
Companies: Columbia University

We investigate the optimal sample complexity of recovering a general high dimensional smooth and sparse function based on point queries. Our result provides a precise characterization of the potential loss in information when restricting to point queries as opposed to the more general linear queries., as well as the benefit of adaption. In addition, we also developed a general framework for function approximation to mitigate the curse of dimensionality that can also be easily adapted to incorporate further structure such as lower order interactions, leading to sample complexities better than those obtained earlier in the literature.

Authors who are presenting talks have a * after their name.

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