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 [1].
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. |