Multivariate Regression with Calibration
Authors: Han Liu, Lie Wang, Tuo Zhao
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. |
| Researcher Affiliation | Academia | Han Liu Department of Operations Research and Financial Engineering Princeton University Lie Wang Department of Mathematics Massachusetts Institute of Technology Tuo Zhao Department of Computer Science Johns Hopkins University |
| Pseudocode | No | The paper describes an algorithm (smoothed proximal gradient algorithm) in prose, detailing its steps and properties, but it does not present it in a formally labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | For a brain activity prediction task, prediction based on the features selected by CMR significantly outperforms that based on the features selected by OMR, and is even competitive with that based on the handcrafted features selected by human experts. (Mitchell et al., Science,2008) |
| Dataset Splits | Yes | We generate a validation set of 200 samples for the regularization parameter selection and a testing set of 10,000 samples to evaluate the prediction accuracy. |
| Hardware Specification | Yes | All simulations are implemented by MATLAB using a PC with Intel Core i5 3.3GHz CPU and 16GB memory. |
| Software Dependencies | No | The paper states, "All simulations are implemented by MATLAB." However, it does not provide a specific version number for MATLAB or any other software dependencies required for replication. |
| Experiment Setup | Yes | The regularization parameter λ of both CMR and OMR is chosen over a grid = 0 240/4λ0, 239/4λ0, , 2 17/4λ0, 2 18/4λ0 , where λ0 = plog d + pm. CMR is solved by the proposed smoothing proximal gradient algorithm, where we set the stopping precision " = 10 4, the smoothing parameter µ = 10 4. OMR is solved by the monotone fast proximal gradient algorithm, where we set the stopping precision " = 10 4. We set p = 2, but the extension to arbitrary p > 2 is straightforward. |