Solving Sparse \& High-Dimensional-Output Regression via Compression
Authors: Renyuan Li, Zhehui Chen, Guanyi Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, numerical results further validate the theoretical findings, showcasing the efficiency and accuracy of the proposed framework. |
| Researcher Affiliation | Collaboration | Renyuan Li Department of Industrial Systems Engineering & Management National University of Singapore renyuan.li@u.nus.edu Zhehui Chen Google zhehuichen@google.com Guanyi Wang Department of Industrial Systems Engineering & Management National University of Singapore guanyi.w@nus.edu.sg |
| Pseudocode | Yes | Algorithm 1 Projected Gradient Descent (for Second Stage) (...) Algorithm 2 Implemented Projected Gradient Descent (for Second Stage) |
| Open Source Code | Yes | The implemented code could be found on Github https://github.com/from-ryan/Solving_ SHORE_via_compression. |
| Open Datasets | Yes | We select two benchmark datasets in multi-label classification, Wiki10-31K and EURLex-4K[5] due to their sparsity property. |
| Dataset Splits | No | The paper mentions splitting the synthetic data into a training set and a testing set ('training set Stra with 80% and a testing set Stest with rest 20%'), but does not explicitly mention a separate validation set for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | All experiments are conducted in Dell workstation Precision 7920 with a 3GHz 48Cores Intel Xeon CPU and 128GB 2934MHz DDR4 Memory. |
| Software Dependencies | Yes | The proposed method and other methods are solved using Py Torch version 2.3.0 and scikit-learn version 1.4.2 in Python 3.12.3. |
| Experiment Setup | Yes | Parameter setting. For synthetic data, we set input dimension d = 104, output dimension K = 2 104, and sparsity-level s = 3. We generate in total n = 3 104, i.i.d. samples (...) We select the number of rows for compressed matrix Φ by m {100, 300, 500, 700, 1000, 2000}. (...) For evaluating the proposed prediction method, Algorithm 2, we pick a fixed stepsize η = 0.9, F = RK + , and set the maximum iteration number as T = 60, and run prediction methods over the set Stest. |