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.
Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations
Authors: Shaocong Ma, Heng Huang
ICLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 25,486 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,127 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 | Through empirical evaluations on both synthetic problems and practical tasks, we demonstrate that DAPs outperform traditional methods under specific conditions. |
| Researcher Affiliation | Academia | Shaocong Ma and Heng Huang Department of Computer Science University of Maryland College Park, MD 20742, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: The algorithm for sampling from a hyper-plane Algorithm 2: A practical implementation of gradient estimator using DAPs |
| Open Source Code | Yes | All source codes, including visualization scripts, are provided with our submission. |
| Open Datasets | Yes | We conducted experiments using the OPT-1.3b model (Zhang et al., 2022) for sentiment classification on the Stanford Sentiment Treebank (SST-2) dataset (Socher et al., 2013). |
| Dataset Splits | Yes | We conducted experiments using the OPT-1.3b model (Zhang et al., 2022) for sentiment classification on the Stanford Sentiment Treebank (SST-2) dataset (Socher et al., 2013). |
| Hardware Specification | Yes | We conducted our experiments on a cluster running RHEL8, equipped with Dual AMD EPYC 9124 processors and eight NVIDIA RTX 6000 Ada Generation graphics cards. |
| Software Dependencies | No | Our code was tested using Python version 3.10.10. Additional dependencies are specified in the supplementary requirements.txt file. |
| Experiment Setup | Yes | Learning rate ฮท: 10 4; Perturbation size ยต: 10 5; Zeroth-order gradient estimation batch size b: 2; Stochastic gradient updates batch size: 16. |