Scalable Approximations for Generalized Linear Problems
Authors: Murat Erdogdu, Mohsen Bayati, Lee H. Dicker
JMLR 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the performance of our algorithm on well-known classification and regression problems, through extensive numerical studies on large-scale datasets, and show that it achieves the highest performance compared to several other widely used optimization algorithms. |
| Researcher Affiliation | Academia | Murat A. Erdogdu EMAIL Department of Computer Science Department of Statistical Sciences University of Toronto Toronto, ON M5S 3G3, Canada Mohsen Bayati EMAIL Graduate School of Business Stanford University Stanford, CA 94305, USA Lee H. Dicker EMAIL Department of Statistics and Biostatistics Rutgers University Piscataway, NJ 08854, USA |
| Pseudocode | Yes | Algorithm 1 SLS: Scaled Least Squares Estimator Algorithm 2 Conversion from one GLM to another |
| Open Source Code | No | The paper does not provide a direct link to a source-code repository or an explicit statement about the release of code for the methodology described. |
| Open Datasets | Yes | The datasets we analyzed were: (i) a synthetic dataset generated from a logistic regression model with iid {exponential(1) 1} predictors scaled by Σ(1); (ii) the Higgs dataset (logistic regression) Baldi et al. (2014); (iii) a synthetic dataset generated from a Poisson regression model with iid binary( 1) predictors scaled by Σ(2); (iv) the Covertype dataset (Poisson regression) Blackard and Dean (1999). |
| Dataset Splits | Yes | The test error is measured as the mean squared error of the estimated mean using the current parameters at each iteration on a test dataset, which is a randomly selected (and set-aside) 10% portion of the entire dataset. |
| Hardware Specification | No | The paper does not specify any particular hardware used for the experiments, such as GPU/CPU models or cloud computing resources. |
| Software Dependencies | No | The paper mentions 'R s built-in function glm' for finding the MLE and various optimization algorithms like Newton-Raphson, BFGS, LBFGS, Gradient descent, and Accelerated gradient descent. However, it does not specify version numbers for these software components or any other libraries used. |
| Experiment Setup | Yes | For all the algorithms for computing the MLE, the step size at each iteration is chosen via the backtracking line search (Boyd and Vandenberghe, 2004). |