SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
Authors: Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 {yih179, ayjain, aorlitsky, varavind}@eng.ucsd.edu |
| 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 |