Stochastic Chebyshev Gradient Descent for Spectral Optimization
Authors: Insu Han, Haim Avron, Jinwoo Shin
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The utility of our methods is demonstrated in numerical experiments.Our experimental results confirm that the proposed algorithms are significantly faster than other competitors under large-scale real-world instances. |
| Researcher Affiliation | Collaboration | 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology 2Department of Applied Mathematics, Tel Aviv University 3AItrics |
| Pseudocode | Yes | Algorithm 1 SGD for solving (4) and Algorithm 2 SVRG for solving (4) |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We use the Movie Lens 1M and 10M datasets [12] ... We benchmark GP regression under natural sound dataset used in [29] and Szeged humid dataset [5] |
| Dataset Splits | No | The paper mentions using Movie Lens 1M and 10M datasets, and natural sound dataset and Szeged humid dataset, but it does not provide specific details about the training, validation, and test splits used. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory specifications, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies along with their version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | No | The paper describes the general algorithms and objective functions but does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rates, batch sizes, epochs), regularization weights, or optimizer settings used in the numerical experiments. |