Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
Authors: Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |