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Record W2399132114 · doi:10.1007/s00037-018-0166-6

The Landscape of Communication Complexity Classes

2018· article· en· W2399132114 on OpenAlexafffund
Mika Göös, Toniann Pitassi, Thomas Watson

Bibliographic record

VenueComputational Complexity · 2018
Typearticle
Languageen
FieldComputer Science
TopicComplexity and Algorithms in Graphs
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaRoyal Society
KeywordsComputer scienceCommunication complexityTheoretical computer science

Abstract

fetched live from OpenAlex

We prove several results which, together with prior work, provide a nearly-complete picture of the relationships among classical communication complexity classes between $${\mathsf{P}}$$ and $${\mathsf{PSPACE}}$$ , short of proving lower bounds against classes for which no explicit lower bounds were already known. Our article also serves as an up-to-date survey on the state of structural communication complexity. Among our new results we show that $${\mathsf{MA} \not\subseteq \mathsf{ZPP}^{\mathsf{NP}[1]}}$$ , that is, Merlin–Arthur proof systems cannot be simulated by zero-sided error randomized protocols with one $${\mathsf{NP}}$$ query. Here the class $$\mathsf{ZPP}^{\mathsf{NP}[1]}$$ has the property that generalizing it in the slightest ways would make it contain $${\mathsf{AM} \cap \mathsf{coAM}}$$ , for which it is notoriously open to prove any explicit lower bounds. We also prove that $${\mathsf{US} \not\subseteq \mathsf{ZPP}^{\mathsf{NP}[1]}}$$ , where $${\mathsf{US}}$$ is the class whose canonically complete problem is the variant of set-disjointness where yes-instances are uniquely intersecting. We also prove that $${\mathsf{US} \not\subseteq \mathsf{coDP}}$$ , where $${\mathsf{DP}}$$ is the class of differences of two $${\mathsf{NP}}$$ sets. Finally, we explore an intriguing open issue: Are rank-1 matrices inherently more powerful than rectangles in communication complexity? We prove a new separation concerning $${\mathsf{PP}}$$ that sheds light on this issue and strengthens some previously known separations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0020.007
Scholarly communication0.0100.018
Open science0.0020.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0110.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.311
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2018
Admission routes2
Has abstractno

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