Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Guided evolutionary strategies: augmenting random search with surrogate gradients
Authors: Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,193 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,045 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we apply our method to example problems, demonstrating an improvement over both standard evolutionary strategies and ο¬rst-order methods that directly follow the surrogate gradient. Figure 1b demonstrates the performance of the method on a toy problem, and is discussed in 4.1. |
| Researcher Affiliation | Industry | 1Google Research, Brain Team, Mountain View, CA, United States. Correspondence to: Niru Maheswaranathan <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Guided Evolutionary Strategies |
| Open Source Code | Yes | For a demo of the method, please see: https://github.com/brain-research/guided-evolutionary-strategies |
| Open Datasets | No | The paper uses generated data (e.g., 'random quadratic problems', 'eigenvalues of the Hessian', 'synthetic gradients') for its experiments, but it does not provide concrete access information (link, DOI, formal citation) for any publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For this, and all of the results in this paper, we set the hyperparameters as Ξ² = 2 and Ξ± = 1 2, as described above. |