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..
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization
Authors: Peilin Zhao, Tong Zhang
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experimental Results |
| Researcher Affiliation | Collaboration | Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Department of Statistics & Biostatistics, Rutgers University, USA; and Big Data Lab, Baidu Research, China |
| Pseudocode | Yes | Algorithm 1 Proximal Stochastic Mirror Descent with Importance Sampling (Iprox-SMD) |
| Open Source Code | No | No explicit statement or link providing access to the open-source code for the methodology described in the paper was found. |
| Open Datasets | Yes | The experiments were performed on several real world datasets downloaded from the LIBSVM website www.csie.ntu.edu.tw/ cjlin/libsvmtools/. The dataset characteristics are provided in the Table 1. |
| Dataset Splits | No | No specific training/test/validation dataset splits were provided. The paper mentions using real-world datasets but does not detail how they were partitioned for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (such as CPU/GPU models, memory, or cloud instance types) used for running the experiments were provided. |
| Software Dependencies | No | No specific ancillary software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or solver versions) were provided. |
| Experiment Setup | Yes | the regularization parameter λ of SVM is set to 10 4, 10 6, 10 4 for ijcnn1, kdd2010(algebra), and w8a, respectively. For prox-SGD and Iprox-SGD, the step size is set to ηt = 1/(λt) for all the datasets. |