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..
Efficient Nonparametric Smoothness Estimation
Authors: Shashank Singh, Simon S. Du, Barnabas Poczos
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical results on simulated data. (Section 8). In this section, we use synthetic data to demonstrate effectiveness of our methods. All experiments use 10, 102, . . . , 105 samples for estimation. |
| Researcher Affiliation | Academia | Shashank Singh Carnegie Mellon University EMAIL Simon S. Du Carnegie Mellon University EMAIL Barnabás Póczos Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks (e.g., labeled 'Algorithm' or 'Pseudocode'). |
| Open Source Code | Yes | MATLAB code for these experiments is available at https://github.com/sss1/Sobolev Estimation. |
| Open Datasets | No | The paper uses 'synthetic data' and 'simulated data' generated from specified distributions (e.g., Gaussians, Uniform) rather than publicly available datasets with specific access information or citations. |
| Dataset Splits | No | The paper mentions using varying numbers of samples for estimation and splitting samples for some calculations, but it does not provide specific train/validation/test dataset splits (percentages, counts, or predefined splits) for reproducibility. |
| Hardware Specification | No | The paper discusses computational efficiency but does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'MATLAB code' but does not provide specific version numbers for MATLAB or any other software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | No | The paper describes the theoretical construction and uses synthetic data, mentioning the number of samples (n) and a smoothing parameter (Zn). However, it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, epochs), optimizer settings, or other system-level training configurations. |