Online Active Regression
Authors: Cheng Chen, Yi Li, Yiming Sun
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The numerical results verify our theoretical results and show that our methods have comparable performance with offline active regression algorithms. |
| Researcher Affiliation | Academia | 1School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. |
| Pseudocode | Yes | Algorithm 1 Online Active Regression for p (1, 2) |
| Open Source Code | No | The paper does not include an unambiguous statement or link to the open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate our algorithm on a realworld dataset, the gas sensor data (Vergara et al., 2012; Rodriguez-Lujan et al., 2014) from the UCI Machine Learning Repository1. |
| Dataset Splits | No | The paper mentions varying budget sizes for label queries and running independent trials, but it does not specify explicit training, validation, or test dataset splits in a conventional supervised learning sense. |
| Hardware Specification | Yes | All our experiments are conducted in MATLAB on a Macbook Pro with an i5 2.9GHz CPU and 8GB of memory. |
| Software Dependencies | No | The paper states that experiments were conducted in MATLAB but does not provide a specific version number for MATLAB or any other software dependencies with versions. |
| Experiment Setup | Yes | We vary the budget sizes for the synthetic data between 800 and 1400 (8% 14% of the data size) and for the realworld data between 1600 and 2500 (12% 18% of the data size). For each budget size, we run 20 independent trials and calculate the mean relative error and standard deviation. |