Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Distributionally Robust Parametric Maximum Likelihood Estimation
Authors: Viet Anh Nguyen, Xuhui Zhang, Jose Blanchet, Angelos Georghiou
NeurIPS 2020 | Venue PDF | 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. |