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[1204.0870] Relax and Localize: From Value to Algorithms
arXiv
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arXiv
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由 A Rakhlin 著作2012被引用 8 次 — Abstract:We show a principled way of deriving online learning algorithms from a minimax analysis. Various upper bounds on the minimax value, ...
[PDF] Relax and Localize: From Value to Algorithms
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Relax and Localize: From Value to Algorithms · A. Rakhlin, Ohad Shamir, Karthik Sridharan · Published in arXiv.org 4 April 2012 · Computer Science, Mathematics.
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Request PDF | Relax and Localize: From Value to Algorithms | We show a principled way of deriving online learning algorithms from a minimax analysis.
Relax and Randomize: From Value to Algorithms
Massachusetts Institute of Technology
http://www.mit.edu › ~rakhlin › papers
Massachusetts Institute of Technology
http://www.mit.edu › ~rakhlin › papers
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We show a principled way of deriving online learning algorithms from a minimax analysis. Various upper bounds on the minimax value, previously thought to be.
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Relax and Localize: From Value to Algorithms
DBLP
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DBLP
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Alexander Rakhlin, Ohad Shamir, Karthik Sridharan: Relax and Localize: From Value to Algorithms. CoRR abs/1204.0870 (2012). manage site settings.
Relax and Randomize : From Value to Algorithms
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2024年10月22日 — Based on the idea of relaxations for deriving algorithms, we provide a template method that takes advantage of localization. Furthermore, we ...
Relax and Localize: From Value to Algorithms
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We show a principled way of deriving online learning algorithms from aminimax analysis. Various upper bounds on the minimax value, previously thoughtto be ...
Relax and Randomize: From Value to Algorithms
ScholarlyCommons
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ScholarlyCommons
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由 A Rakhlin 著作2012被引用 88 次 — We show a principled way of deriving online learning algorithms from a minimax analysis. Various upper bounds on the minimax value, previously thought to be.
Convex Optimization in Sensor Network Localization and ...
The Hong Kong Polytechnic University (PolyU)
https://www.polyu.edu.hk › profile › pong › Talks
The Hong Kong Polytechnic University (PolyU)
https://www.polyu.edu.hk › profile › pong › Talks
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由 TK Pong 著作2011 — Problem is NP-hard (reduction from PARTITION). • Aim 1: Tractability – use a convex relaxation. • Aim 2: Identify sensors correctly positioned by relaxation.
Localization and Adaptation in Online Learning
Proceedings of Machine Learning Research
http://proceedings.mlr.press › ...
Proceedings of Machine Learning Research
http://proceedings.mlr.press › ...
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由 A Rakhlin 著作2013被引用 5 次 — We introduce a formalism of localization for online learning problems, which, similarly to statistical learning theory, can be used to ob- tain fast rates.