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..
Explicit Gradient Learning for Black-Box Optimization
Authors: Elad Sarafian, Mor Sinay, Yoram Louzoun, Noa Agmon, Sarit Kraus
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate EGL and achieve state-of-the-art results in two challenging problems: (1) the COCO test suite against an assortment of standard BBO methods; and (2) in a high-dimensional non-convex image generation task. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Bar Ilan University, Israel. Correspondence to: Elad Sarafian, Mor Sinay <elad.sarafian@gmail.com, EMAIL> |
| Pseudocode | Yes | Algorithm 1 Indirect Gradient Learning (...) Algorithm 2 Convergent EGL |
| Open Source Code | Yes | The code is available at http://github.com/Mor Sinay/BBO. |
| Open Datasets | Yes | We tested EGL on the COCO test suite (...) We trained a generator & discriminator for the Celeb A dataset (Liu et al., 2015) |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits, percentages, or references to predefined splits needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware components (e.g., GPU/CPU models, memory details) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Scipy and Cma Python packages' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For additional information, including a hyperparameters list, refer to Appendix Sec. F. |