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..
GAGA: Deciphering Age-path of Generalized Self-paced Regularizer
Authors: Xingyu Qu, Diyang Li, Xiaohan Zhao, Bin Gu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of GAGA on real-world datasets, and find considerable speedup between our algorithm and competing baselines. |
| Researcher Affiliation | Academia | 1 Mohamed bin Zayed University of Artificial Intelligence 2 Nanjing University of Information Science & Technology |
| Pseudocode | Yes | Algorithm 1 Alternate Convex Search (ACS) and Algorithm 2 Generalized Age-path Algorithm (GAGA) |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | Dataset Source Samples Dimensions Task mfeat-pixel UCI [43] 2000 240 C pendigits UCI 3498 16 hiva agnostic Open ML 4230 1620. UCI [43], Open ML [44]. |
| Dataset Splits | No | No explicit specification of training/validation/test dataset splits (e.g., percentages, counts, or explicit cross-validation strategy) in the main body of the paper. |
| Hardware Specification | Yes | All experiments are run on a machine with Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz, 16 GB RAM and NVIDIA GeForce GTX 1080Ti. |
| Software Dependencies | Yes | The codes are implemented in Python 3.8 based on the Scikit-learn (version 0.24) library [57] and Matplotlib (version 3.4.3) for visualization. For comparison, ACS is implemented via the open source Geatpy (version 2.7.0) framework [58]. The ODE solver is based on SciPy (version 1.7.3) using LSODE routine [54, 55]. |
| Experiment Setup | Yes | For SVM, we use the Gaussian kernel K(x1, x2) = exp( γκ x1 x2 2). For linear SP-regularizer, λmin and λmax are set as 1e-4 and 500 respectively. The trade-off parameters C for SVM and α for Lasso are searched from {1e-3, 1e-2, . . . , 1e2} and {1e-5, 1e-4, . . . , 1e-1} respectively. The kernel coefficient γκ is searched from {0.1, 0.2, . . . , 2}. |