MétaCan
Menu
Back to cohort
Record W4393562725 · doi:10.5281/zenodo.4110025

Near-optimal robust bilevel linear instances

2020· dataset· en· W4393562725 on OpenAlexaff
Mathieu Besançon, Miguel F. Anjos, Luce Brotcorne

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldComputer Science
TopicOptimization and Variational Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBilevel optimizationComputer scienceMathematicsMathematical optimizationAlgorithmOptimization problem

Abstract

fetched live from OpenAlex

This repository contains the bilevel instances used in the paper Near-Optimal Robust Bilevel Optimization (https://arxiv.org/abs/1908.04040). They come in four groups, small, medium, large and mips instances, with different numbers of variables and constraints as described in the paper. The MIPS instances are constructed from the MIPS/RANDOM instances from https://coral.ise.lehigh.edu/data-sets/bilevel-instances/ The /data folder contains instances in JLD format, the DataReader folder contains a Julia project for reading the data.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0040.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.026

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 head0.051
GPT teacher head0.249
Teacher spread0.198 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicOptimization and Variational AnalysisFrench-language works237,207