Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Total Least Squares Regression in Input Sparsity Time
Authors: Huaian Diao, Zhao Song, David Woodruff, Xin Yang
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,910 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,935 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our algorithm on real and synthetic data sets. |
| Researcher Affiliation | Academia | Huaian Diao Northeast Normal University & KLAS of MOE EMAIL Zhao Song University of Washington EMAIL David P. Woodruff Carnegie Mellon University EMAIL Xin Yang University of Washington EMAIL |
| Pseudocode | Yes | Algorithm 1 Our Fast Total Least Squares Algorithm |
| Open Source Code | Yes | 1The code can be found at https://github.com/yangxinuw/total_least_squares_code. |
| Open Datasets | Yes | We also conducted experiments on real datasets from the UCI Machine Learning Repository [DKT17]. ... We have four real datasets : Airfoil Self-Noise [UCIa] in Table 2(a), Wine Quality Red wine [UCIc, CCA+09] in Table 2(b), Wine Quality White wine [UCIc, CCA+09] in Table 2(c), Insurance Company Benchmark (COIL 2000) Data Set [UCIb, PS] |
| Dataset Splits | No | The paper discusses synthetic data and real datasets from UCI, and specifies 'sample density' for sketching, but does not provide explicit training/test/validation split percentages or sample counts for the datasets used in experiments. |
| Hardware Specification | Yes | Our numerical tests are carried out on an Intel Xeon E7-8850 v2 server with 2.30GHz and 4GB RAM |
| Software Dependencies | Yes | under Matlab R2017b. |
| Experiment Setup | Yes | In the experiments, we take sample density ρ = 0.1, 0.3, 0.6, 0.9 respectively to check our performance. |