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..
Global optimization of Lipschitz functions
Authors: Cédric Malherbe, Nicolas Vayatis
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | a numerical assessment is provided at the end of the paper to illustrate the potential of this strategy with respect to state-of-the-art methods over typical benchmark problems for global optimization. In Section 5, Experiments are conducted to compare the empirical performance of Ada LIPO with five state-of-the-art global optimization methods on various datasets and synthetic problems. |
| Researcher Affiliation | Academia | 1CMLA, ENS Cachan, CNRS, Universit e Paris-Saclay, 94235, Cachan, France. |
| Pseudocode | Yes | Algorithm 1 LIPO(n, k, X, f) and Algorithm 2 ADALIPO(n, p, ki Z, X, f) provide structured pseudocode for the proposed algorithms. |
| Open Source Code | No | The paper does not explicitly state that the source code for LIPO or Ada LIPO is open-sourced or provide a link to it. It only mentions the use of third-party libraries for comparison algorithms: In Python 2.7 from Bayes Opt (Martinez-Cantin, 2014), CMA 1.1.06 (Hansen, 2011) and NLOpt (Johnson, 2014). |
| Open Datasets | Yes | We first studied the task of estimating the regularization parameter λ and the bandwidth σ of a gaussian kernel ridge regression minimizing the empirical mean squared error of the predictions over a 10-fold cross validation with real data sets. The optimization was performed over (ln(λ), ln(σ)) [ 3, 5] [ 2, 2] with five data sets from the UCI Machine Learning Repository (Lichman, 2013): Auto-MPG, Breast Cancer Wisconsin (Prognostic), Concrete slump test, Housing and Yacht Hydrodynamics. We then compared the algorithms on a series of five synthetic problems commonly met in standard optimization benchmark taken from (Jamil & Yang, 2013; Surjanovic & Bingham, 2013): Holder Table, Rosenbrock, Sphere, Linear Slope and Deb N.1. |
| Dataset Splits | Yes | minimizing the empirical mean squared error of the predictions over a 10-fold cross validation with real data sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory amounts) used for running its experiments. |
| Software Dependencies | Yes | In Python 2.7 from Bayes Opt (Martinez-Cantin, 2014), CMA 1.1.06 (Hansen, 2011) and NLOpt (Johnson, 2014). |
| Experiment Setup | Yes | For a fair comparison, the tuning parameters were all set to default and Ada LIPO was constantly used with a parameter p set to 0.1 and a sequence ki = (1 + 0.01/d)i fixed by an arbitrary rule of thumb. |