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Adaptive reduced rank regression

Abstract:

We study the low rank regression problem y = Mx + ε, where x and y are d1 and d2 dimensional vectors respectively. We consider the extreme high-dimensional setting where the number of observations n is less than d1 + d2. Existing algorithms are designed for settings where n is typically as large as rank(M)(d1+d2). This work provides an efficient algorithm which only involves two SVD, and establishes statistical guarantees on its performance. The algorithm decouples the problem by first estima...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Role:
Author
Publisher:
Curran Associates
Host title:
Advances in Neural Information Processing Systems 33: 34th Conference on Neural Information Processing Systems (NeurIPS 2020)
Volume:
33
Pages:
4103-4114
Publication date:
2021-07-01
Acceptance date:
2020-09-26
Event title:
34th Conference on Neural Information Processing Systems (NeurIPS 2020)
Event location:
Virtual Event
Event website:
https://nips.cc/Conferences/2020
Event start date:
2020-12-06
Event end date:
2020-12-12
ISSN:
1049-5258
ISBN:
9781713829546
Language:
English
Keywords:
Pubs id:
1173043
Local pid:
pubs:1173043
Deposit date:
2021-07-13

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