Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models
Authors: Hongchang Gao, Heng Huang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results have verified the efficiency and correctness of our proposed method. |
| Researcher Affiliation | Academia | Hongchang Gao, Heng Huang Department of Electrical and Computer Engineering, University of Pittsburgh, USA hongchanggao@gmail.com, heng.huang@pitt.edu |
| Pseudocode | Yes | Algorithm 1 Stochastic L-BFGS Algorithm for Solving Eq. (1). |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | For the sparse linear regression model, we evaluate its performance on E2006-TFIDF dataset... For the sparse logistic regression model, we evaluate its classification performance on the RCV1-Binary dataset... Note that both datasets are available at the LIBSVM website 1https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets |
| Dataset Splits | No | The paper mentions training and testing data points for E2006-TFIDF (16,087 training, 3,308 testing) and RCV1-Binary (20,242 training, 677,399 testing, with a selected testing set of 5000 samples from each class), but does not explicitly provide details about a validation set or specific splitting methodology beyond providing total counts for train and test. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that are needed to replicate the experiment. |
| Experiment Setup | Yes | Additionally, we set L = 10, M = 10, |B| = 10, and |B | = 50. The step length of each method is chosen to achieve the best performance... we set σ2 = 0.01... Toy-1 is with n = 20000, d = 2000, s = 100, s = 200, Σ = I. Toy-2 is with n = 50000, d = 5000, s = 500, s = 1000... the sparsity level s is set as 2000... we set the sparsity level s as 500. |