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 $k$-Sparse Band–Limited Interpolation with Improved Approximation Ratio
Authors: Yang Cao, Xiaoyu Li, Zhao Song, Chiwun Yang
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Question: Does the paper include experiments? Answer: [NA] Justification: The paper does not include experiments. |
| Researcher Affiliation | Academia | Yang Cao Wyoming Seminary EMAIL Xiaoyu Li University of New South Wales EMAIL Zhao Song University of California, Berkeley EMAIL Chiwun Yang Sun Yat-sen University EMAIL |
| Pseudocode | Yes | Algorithm 1 Discrete 1-D Fourier Set-Query Algorithm 2 A well-balanced sampling procedure based on Randomized BSS (see Chen and Price (2019a)) Algorithm 3 Our fast implementation of well-balanced sampling procedure Algorithm 4 Quadratic-form sampling data structure Algorithm 5 Quadratic-form sampling with preprocessing-query trade-off: Preprocessing Algorithm 6 Quadratic-form sampling with preprocessing-query trade-off: Query Algorithm 7 Fast distillation for one-dimensional signal Algorithm 8 Signal estimation algorithm for one-dimensional signals (sample optimal version) Algorithm 9 Signal estimation algorithm for one-dimensional signals (high-accuracy version) |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: The paper does not include experiments requiring code. |
| Open Datasets | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: The paper does not include experiments requiring code. |
| Dataset Splits | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: The paper does not include experiments. |
| Hardware Specification | No | Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] Justification: The paper does not include experiments. |
| Software Dependencies | No | The paper does not explicitly state any software dependencies with specific version numbers for its methodology. It is a theoretical paper and the NeurIPS checklist indicates no experiments are included. |
| Experiment Setup | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: The paper does not include experiments. |