Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A ranking approach to global optimization
Authors: Cedric Malherbe, Emile Contal, Nicolas Vayatis
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Eventually, numerical evidence is given to show that the main algorithm of the paper which adapts empirically to the underlying ranking structure essentially outperforms existing state-of-the-art global optimization algorithms in typical benchmarks. |
| Researcher Affiliation | Academia | CMLA, ENS Cachan, CNRS, Universit e Paris-Saclay, 94235, Cachan, France |
| Pseudocode | Yes | Algorithm 1 RANKOPT(n, f, X, R) and Algorithm 2 ADARANKOPT(n, f, X, p, {RN}N N ) |
| Open Source Code | No | The paper does not provide concrete access to its own source code, only refers to a third-party library it uses (NLOpt). |
| Open Datasets | No | The algorithms were compared on three synthetic problems. No mention of specific datasets or their public availability beyond these synthetic functions. |
| Dataset Splits | No | The algorithms were compared on three synthetic problems and then describes maximizing/minimizing those functions directly. No explicit mention of train/validation/test splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions the 'NLOpt library (Johnson, 2014)' as a tool used for comparison, but it does not provide specific version numbers for its own software dependencies. |
| Experiment Setup | Yes | The tuning parameters were set to default and the parameter p was set to 1/4 for the convex ranking rules and to 1/10 for the polynomial ranking rules. |