Regression Learning with Limited Observations of Multivariate Outcomes and Features
Authors: Yifan Sun, Grace Yi
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments show that our approach outperforms methods that apply existing algorithms for univariate outcome individually to each coordinate of multivariate outcomes in a naive manner. Further, utilizing the L1 loss function or introducing a Lasso-type penalty can enhance predictions in the presence of outliers or high dimensional features. This research contributes valuable insights into addressing the challenges posed by incomplete data. and 5. Experiments section |
| Researcher Affiliation | Academia | 1Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Canada 2Department of Computer Science, University of Western Ontario, London, Canada. |
| Pseudocode | Yes | Algorithm 1 Multivariate Least Squares Ridge Regression, Algorithm 2 Multivariate Least Squares Lasso Regression, Algorithm 3 Multivariate Least Absolute Deviations Ridge Regression, Algorithm 4 Multivariate Least Absolute Deviations Lasso Regression, Algorithm 5 Multivariate AERR |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We apply the proposed method to the yeast cell dataset, available from R package spls. The dataset can be accessed at http://jeffgoldsmith.com/IWAFDA/shortcourse_data.html. |
| Dataset Splits | Yes | We randomly split the entire sample into training data and test data using 10 fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'R package mice' and 'R package spls' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In implementing all methods, B is set to be 100. By Theorems 3.1, the optimal step size for Algorithm 1 is 2(p0 1) / (T p (1+q/q0)) . Hence, the step size for LSR is set to 2(p0 1) / (T p (1+q/q0)). The two tuning parameters in (7), λ1 and λ2, are set to 0.1 and 0.001, respectively. |