Conference item
Pseudo-derandomizing learning and approximation
- Abstract:
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We continue the study of pseudo-deterministic algorithms initiated by Gat and Goldwasser [Eran Gat and Shafi Goldwasser, 2011]. A pseudo-deterministic algorithm is a probabilistic algorithm which produces a fixed output with high probability. We explore pseudo-determinism in the settings of learning and approximation. Our goal is to simulate known randomized algorithms in these settings by pseudo-deterministic algorithms in a generic fashion - a goal we succinctly term pseudo-derandomization....
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Version of record, pdf, 519.2KB)
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- Publisher copy:
- 10.4230/LIPIcs.APPROX-RANDOM.2018.55
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Authors
Funding
Bibliographic Details
- Publisher:
- Schloss Dagstuhl Publisher's website
- Journal:
- 22nd International Conference on Randomization and Approximation Journal website
- Volume:
- 116
- Series:
- Leibniz International Proceedings in Informatics
- Host title:
- Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2018)
- Publication date:
- 2018-08-02
- Acceptance date:
- 2018-06-01
- DOI:
- ISSN:
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1868-8969
- Source identifiers:
-
908930
- ISBN:
- 9783959770859
Item Description
- Keywords:
- Pubs id:
-
pubs:908930
- UUID:
-
uuid:c7f33f22-d7aa-41ae-aa7d-a4f37c1a1f18
- Local pid:
- pubs:908930
- Deposit date:
- 2018-08-18
Terms of use
- Copyright holder:
- Olivieira and Santhanam
- Copyright date:
- 2018
- Notes:
-
Copyright © Igor C. Oliveira and Rahul Santhanam;
licensed under Creative Commons License CC-BY.
- Licence:
- Other
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