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  1. Transactions on Computation Theory (TOCT)
  2. ACM Transactions on Computation Theory (TOCT) : Volume 7
  3. Issue 1, December 2014
  4. Advice Lower Bounds for the Dense Model Theorem
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ACM Transactions on Computation Theory (TOCT) : Volume 9
ACM Transactions on Computation Theory (TOCT) : Volume 8
ACM Transactions on Computation Theory (TOCT) : Volume 7
Issue 4, September 2015
Issue 3, July 2015
Issue 2, May 2015
Issue 1, December 2014
Advice Lower Bounds for the Dense Model Theorem
Limitations of Local Filters of Lipschitz and Monotone Functions
The Complexity of the Nucleolus in Compact Games
Kernel Lower Bounds using Co-Nondeterminism: Finding Induced Hereditary Subgraphs
ACM Transactions on Computation Theory (TOCT) : Volume 6
ACM Transactions on Computation Theory (TOCT) : Volume 5
ACM Transactions on Computation Theory (TOCT) : Volume 4
ACM Transactions on Computation Theory (TOCT) : Volume 3
ACM Transactions on Computation Theory (TOCT) : Volume 2
ACM Transactions on Computation Theory (TOCT) : Volume 1

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Advice Lower Bounds for the Dense Model Theorem

Content Provider ACM Digital Library
Author Watson, Thomas
Copyright Year 2015
Abstract We prove a lower bound on the amount of nonuniform advice needed by black-box reductions for the Dense Model Theorem of Green, Tao, and Ziegler, and of Reingold, Trevisan, Tulsiani, and Vadhan. The latter theorem roughly says that for every distribution $\textit{D}$ that is Δ-dense in a distribution that is $\textit{ε}′-indistinguishable$ from uniform, there exists a “dense model” for $\textit{D},$ that is, a distribution that is $\textit{Δ}-dense$ in the uniform distribution and is $\textit{ε}-indistinguishable$ from $\textit{D}.$ This $\textit{ε}-indistinguishability$ is with respect to an arbitrary small class of functions $\textit{F}.$ For the natural case where $\textit{ε}′$ ≥ $Ω(\textit{εΔ})$ and $\textit{ε}$ ≥ $\textit{Δ}\textit{O}(1),$ our lower bound implies that $Ω(√(1/\textit{ε})$ $log(1/\textit{Δ})·log|\textit{F}|)$ advice bits are necessary for a certain type of reduction that establishes a stronger form of the Dense Model Theorem (and which encompasses all known proofs of the Dense Model Theorem in the literature). There is only a polynomial gap between our lower bound and the best upper bound for this case (due to Zhang), which is $\textit{O}((1/\textit{ε}2)log(1/\textit{Δ})·log|\textit{F}|).$ Our lower bound can be viewed as an analogue of list size lower bounds for list-decoding of error-correcting codes, but for “dense model decoding” instead.
Starting Page 1
Ending Page 18
Page Count 18
File Format PDF
ISSN 19423454
e-ISSN 19423462
DOI 10.1145/2676659
Volume Number 7
Issue Number 1
Journal ACM Transactions on Computation Theory (TOCT)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-01-13
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Advice Dense model theorem Lower bounds
Content Type Text
Resource Type Article
Subject Computational Theory and Mathematics Theoretical Computer Science
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