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..
On the Second-order Convergence Properties of Random Search Methods
Authors: Aurelien Lucchi, Antonio Orvieto, Adamos Solomou
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithm empirically and ๏ฌnd good agreements with our theoretical results. |
| Researcher Affiliation | Academia | Aurelien Lucchi Antonio Orvieto Adamos Solomou Department of Computer Science ETH Zurich |
| Pseudocode | Yes | Algorithm 1 TWO-STEP RANDOM SEARCH (RS). Similar to the STP method [6], but we alternate between two perturbation magnitudes: ฯ1 is set to be optimal for the large gradient case, while ฯ2 optimal to escape saddles. |
| Open Source Code | Yes | the code for reproducing the experiments is available online5. |
| Open Datasets | No | The paper uses synthetic functions (e.g., 'Function with growing dimension' and 'Rastrigin function') for its experiments, which are generated and not referenced as publicly available datasets with access information. |
| Dataset Splits | No | The paper uses synthetic functions for optimization tasks and does not specify training, validation, or test dataset splits in the conventional sense. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | For each task, the hyperparameters of every method are selected based on a coarse grid search re๏ฌned by trial and error. We choose to run DFPI for 20 iterations for all the results shown in the paper. |