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..
Efficient Hyper-parameter Optimization with Cubic Regularization
Authors: Zhenqian Shen, Hansi Yang, Yong Li, James Kwok, Quanming Yao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world data demonstrate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1Department of Electronic Engineering, Tsinghua University, Beijing, China 2Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China |
| Pseudocode | Yes | Algorithm 1 Hyper-parameter optimization with cubic regularization. |
| Open Source Code | No | The paper does not include an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use CIFAR-10 dataset for experiments with 50k, 5k, 5k image samples as training, validation, test data, respectively.Two well-known knowledge graph datasets, FB15k237 [38] and WN18RR [39], are used in experiments and their statistics are in Appendix E.2. |
| Dataset Splits | Yes | We use CIFAR-10 dataset for experiments with 50k, 5k, 5k image samples as training, validation, test data, respectively. |
| Hardware Specification | Yes | Experiments are conducted on a 24GB NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | For the mask of i-th dimension zi, we use sigmoid function to represent the probability to mask that dimension, i.e., p i(zi = 1) = 1/(1+exp( i)) and p i(zi = 0) = 1 p i(zi = 1). As all hyper-parameters considered in this application are discrete, we choose softmax-liked distributions to represent the probability to select a specific value for each hyper-parameter. The hyper-parameter z is divided into two parts: z ( , {βi}), and Rz(t) is parameterized as follows: Rz(t) PI i=1 iri(t; βi) |