Distributionally Robust Parametric Maximum Likelihood Estimation
Authors: Viet Anh Nguyen, Xuhui Zhang, Jose Blanchet, Angelos Georghiou
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now showcase the abilities of the proposed framework in the distributionally robust Poisson and logistic regression settings using a combination of simulated and empirical experiments. 5 Numerical Experiments |
| Researcher Affiliation | Academia | Viet Anh Nguyen Xuhui Zhang Jos e Blanchet Stanford University, United States EMAIL Angelos Georghiou University of Cyprus, Cyprus EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The MATLAB code is available at https://github.com/angelosgeorghiou/DR-Parametric-MLE. |
| Open Datasets | Yes | using data sets from the UCI repository [13]. |
| Dataset Splits | Yes | In each independent trial, we randomly split the data into train-validation-test set with proportion 50%-25%-25%. |
| Hardware Specification | Yes | on an Intel i7 CPU (1.90GHz) computer. |
| Software Dependencies | Yes | modeled in MATLAB using CVX [16] and solved by the exponential conic solver MOSEK [24]. (Referencing [16] 'CVX: Matlab software for disciplined convex programming, version 2.1, Mar. 2014.' and [24] 'MOSEK Ap S. The MOSEK optimization toolbox. Version 9.2., 2019.') |
| Experiment Setup | Yes | We calibrate the regression model (12) by tuning ρc = a N 1 c with a [10 4, 1] and ε [PC c=1 bpcρc, 1], both using a logarithmic scale with 20 discrete points. we calibrate the regression model (13) by tuning ρc = a N 1 c with a [10 4, 10] using a logarithmic scale with 10 discrete points and setting ε = 2 PC c=1 bpcρc. |