Beyond the Signs: Nonparametric Tensor Completion via Sign Series
Authors: Chanwoo Lee, Miaoyan Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 chanwoo.lee@wisc.edu Miaoyan Wang Department of Statistics University of Wisconsin-Madison miaoyan.wang@wisc.edu |
| 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. |