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..
Derivative Estimation in Random Design
Authors: Yu Liu, Kris De Brabanter
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the simulation study, we show that the new estimator has similar performance compared to local polynomial regression and penalized smoothing splines. |
| Researcher Affiliation | Academia | Yu Liu1, Kris De Brabanter1,2 1Department of Computer Science, 2Department of Statistics Iowa State University, Ames, IA 50011, USA. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper cites external tools (ks, locpol, pspline) with their URLs, but there is no explicit statement from the authors about releasing the source code for their own described methodology. |
| Open Datasets | No | The paper uses simulated data generated according to mathematical functions (e.g., 'm(X) = cos2(2πX) for X beta(2, 2)', 'X(1 X) sin{(2.1π)/(X + 0.05)} for X U(0.25, 1)'). It does not use or provide access to a public, external dataset. |
| Dataset Splits | No | The paper constructs data sets for Monte Carlo simulations ('constructed data sets of size n = 700 and generated the functions... 100 times'), but it does not specify explicit train/validation/test splits for these generated datasets or any cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., CPU, GPU models, or memory). |
| Software Dependencies | Yes | The paper mentions specific software with version numbers: 'ks: Kernel smoothing v1.11.1', 'locpol: Kernel local polynomial regression v0.6', 'pspline: Penalized smoothing splines v1.0-18'. |
| Experiment Setup | Yes | The tuning parameter k is selected over the integer set [1, (n - 1)/2 ] and according to Corollary 2. We use local cubic regression (p = 3) with bimodal kernel to initially smooth the data. Bandwidths for the bimodal kernel ˆhb are selected from the set {0.1, 0.105, 0.11, . . . , 0.2} and corrected for a unimodal Gaussian kernel. Bandwidths are selected from the set {0.04, 0.045, . . . , 0.08} and corrected for a unimodal Gaussian kernel. The order of the local polynomial is set to p = 2... sample size n = 700 and e N(0, 0.22)... constructed data sets of size n = 700 and generated the functions... 100 times according to model (2) with e N(0, 0.22) and e N(0, 0.32) for model (12) and model (13) respectively. |