Consistent Robust Regression
Authors: Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments were carried out on synthetically generated linear regression datasets with corruptions. All implementations were done in Matlab and were run on a single core 2.4GHz machine with 8GB RAM. The experiments establish the following: 1) CRR gives consistent estimates of the regression model, especially in situations with a large number of corruptions where the ordinary least squares estimator fails catastrophically, 2) CRR scales better to large datasets than the TORRENT-FC algorithm of [3] (upto 5 faster) and the Extended Lasso algorithm of [17] (upto 20 faster). |
| Researcher Affiliation | Collaboration | Kush Bhatia University of California, Berkeley kushbhatia@berkeley.edu Prateek Jain Microsoft Research, India prajain@microsoft.com Parameswaran Kamalaruban EPFL, Switzerland kamalaruban.parameswaran@epfl.ch Purushottam Kar Indian Institute of Technology, Kanpur purushot@cse.iitk.ac.in |
| Pseudocode | Yes | Algorithm 1 CRR: Consistent Robust Regression |
| Open Source Code | No | The paper does not provide any links to open-source code repositories or explicitly state that the source code for their methodology is publicly available. |
| Open Datasets | No | The paper uses synthetically generated data and does not provide access information (link, citation, or repository) for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses synthetically generated data but does not specify any training, validation, or test dataset splits (e.g., percentages, sample counts, or defined methodologies for partitioning the data). |
| Hardware Specification | Yes | All implementations were done in Matlab and were run on a single core 2.4GHz machine with 8GB RAM. |
| Software Dependencies | No | The paper states 'All implementations were done in Matlab' but does not specify a version number for Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Data: The model w Rd was chosen to be a random unit norm vector. The data was generated as xi N(0, Id). The k responses to be corrupted were chosen uniformly at random and the value of the corruptions was sets as b i Unif (10, 20). Responses were then generated as yi = xi, w + ηi + b i where ηi N(0, σ2). All reported results were averaged over 20 randomly trials. Evaluation Metric: We measure the performance of various algorithms using the standard L2 error: r bw = bw w 2. For the timing experiments, we deemed an algorithm to converge on an instance if it obtained a model wt such that wt wt 1 2 10 4. |