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..
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
Authors: Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, SURF outperforms state-of-the art algorithms. and We conclude in Section 5 with a detailed comparison of SURF and ADLS, and show experimental results that confirm the theory and show that SURF performs well for a variety of distributions. and Our experiments show that SURF is more adaptive than ADLS, and perform additional experiments on both synthetic and real datasets. |
| Researcher Affiliation | Academia | Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar Dept. of Electrical and Computer Engineering University of California, San Diego EMAIL |
| Pseudocode | No | The paper describes the routines INT, MERGE, and COMP in text, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it state that the code is available. |
| Open Datasets | Yes | Finally, we ran SURF on real data sets consisting of salaries from the 1994 US census and electric signals from the sensorless drive diagnosis dataset [8], that have been used to evaluate classification algorithms [10, 4, 14]. |
| Dataset Splits | No | The paper mentions evaluating on 'a test set with one-fourth the number of samples' but does not provide specific training, validation, or the full partitioning details needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of 'MATLAB R' for a function, but it does not specify version numbers for MATLAB or any other key software dependencies. |
| Experiment Setup | Yes | SURF is run with α = 0.25 and the errors are averaged over 10 runs. and Evaluation of the estimate output by SURF with degrees d = 1, 2, 3, α = 0.25 |