Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities
Authors: Yunwen Lei, Ke Tang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 leiyw@sustc.edu.cn tangk3@sustc.edu.cn |
| 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. |