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 Composite Mirror Descent: Optimal Bounds with High Probabilities
Authors: Yunwen Lei, Ke Tang
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results are reported to support our theoretical findings. ... 6 Simulations: In this section, we include some experimental results to validate these theoretical findings. We apply SGD (4.1) with a linear kernel Kx = x and the hinge loss ℓ(a, y) = max{0, 1 ya} to several binary classification datasets (ADULT, GISETTE, IJCNN, MUSHROOMS, PHISHING and SPLICE). |
| Researcher Affiliation | Academia | Yunwen Lei and Ke Tang Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China EMAIL EMAIL |
| Pseudocode | No | The paper describes algorithms and update rules in text (e.g., equation 2.2) but does not provide any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We apply SGD (4.1)... to several binary classification datasets (ADULT, GISETTE, IJCNN, MUSHROOMS, PHISHING and SPLICE). All these datasets, described in Supplementary Material G, can be download from the LIBSVM website [8]. [8] C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3):27, 2011. |
| Dataset Splits | No | The paper mentions training on datasets and plotting "test errors," but it does not specify any dataset splits (e.g., percentages for training, validation, and test sets) or describe a cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions) used in the experiments. |
| Experiment Setup | Yes | We consider polynomially decaying step sizes of the form ηt = 5t θ with θ {0.25, 0.51, 0.75} (we consider θ = 0.51, instead of θ = 0.5, since the associated step size sequence is square-summable). We repeat experiments 12 times and report the average of results. |