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.
Beyond the Signs: Nonparametric Tensor Completion via Sign Series
Authors: Chanwoo Lee, Miaoyan Wang
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,640 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,868 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 demonstrate the outperformance of our approach over previous methods on two datasets, one on human brain connectivity networks and the other on topic data mining. |
| Researcher Affiliation | Academia | Chanwoo Lee Department of Statistics University of Wisconsin-Madison EMAIL Miaoyan Wang Department of Statistics University of Wisconsin-Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 Nonparametric tensor completion via learning reduction |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We apply our method to two tensor datasets, the MRN-114 human brain connectivity data [38], and NIPS data [19]. |
| Dataset Splits | Yes | Reported MAEs are averaged over five runs of cross-validation, with 20% entries for testing and 80% for training. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions general software components like "hinge loss" but does not specify software names with version numbers needed for replication. |
| Experiment Setup | Yes | We consider order-3 tensors of equal dimension, and set d {15, 20, . . . , 55, 60}, r {2, 3, . . . , 10}, H = 10+(d 15)/5 in Algorithm 1. ... Reported MAEs are averaged over five runs of cross-validation, with 20% entries for testing and 80% for training. |