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..
Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
Authors: Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments. For illustration purposes, Fig. 3 provides some numerical experiments on different no-regret policies discussed in the rest of our paper. |
| Researcher Affiliation | Collaboration | Amélie Héliou 1 Matthieu Martin 1 Panayotis Mertikopoulos 2 1 Thibaud Rahier 1 Criteo AI Lab 2Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France. |
| Pseudocode | No | The paper defines algorithms like DAX and HDA through mathematical equations and descriptions of their components, but it does not present them in a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper does not mention using a specific named public dataset for its numerical experiments. It describes the adversarial function as "analytic and randomly drawn" which suggests a synthetic setup rather than a public dataset. |
| Dataset Splits | No | The paper does not mention using specific training, validation, or test dataset splits. This information is typically provided when empirical evaluation is performed on well-defined datasets, which are not detailed here. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper mentions that "First the Hierarchical method is as outlined in Section 4 with parameters of the algorithm described below" and "We present the full details of our experiments in Appendix D." However, the provided text does not include Appendix D or the specific parameter values, making it impossible to determine concrete setup details from this excerpt. |