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Record W3128277790 · doi:10.1093/imrn/rnab282

Jensen’s Inequality for Separately Convex Noncommutative Functions

2021· preprint· en· W3128277790 on OpenAlex

Why this work is in the frame

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affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Mathematics Research Notices · 2021
Typepreprint
Languageen
FieldMathematics
TopicRandom Matrices and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMathematicsProbability measureJensen's inequalityNoncommutative geometryFree probabilityCombinatoricsMeasure (data warehouse)Convex functionRegular polygonConvex setConvex analysisDiscrete mathematicsPure mathematicsConvex optimizationGeometry

Abstract

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Abstract Classically, Jensen’s Inequality asserts that if $X$ is a compact convex set, and $f:K\to {\mathbb {R}}$ is a convex function, then for any probability measure $\mu $ on $K$, that $f(\text {bar}(\mu ))\le \int f\; \text {d}\mu $, where $\text {bar}(\mu )$ is the barycenter of $\mu $. Recently, Davidson and Kennedy proved a noncommutative (“nc”) version of Jensen’s inequality that applies to nc convex functions, which take matrix values, with probability measures replaced by ucp maps. In the classical case, if $f$ is only a separately convex function, then $f$ still satisfies the Jensen inequality for any probability measure that is a product measure. We prove a noncommutative Jensen inequality for functions that are separately nc convex in each variable. The inequality holds for a large class of ucp maps that satisfy a noncommutative analogue of Fubini’s theorem. This class of ucp maps includes any free product of ucp maps built from Boca’s theorem, or any ucp map that is conditionally free in the free-probabilistic sense of Młotkowski. As an application to free probability, we obtain some operator inequalities for conditionally free ucp maps applied to free semicircular families.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.445
GPT teacher head0.556
Teacher spread0.111 · 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