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..
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
Authors: Insu Han, Haim Avron, Jinwoo Shin
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we report the empirical results of POLY-TENSORSKETCH for the element-wise matrix functions under various machine learning applications. |
| Researcher Affiliation | Academia | 1School of Electrical Engineering, KAIST, Daejeon, Korea 2School of Mathematical Sciences, Tel Aviv University, Israel 3Graduate School of AI, KAIST, Daejeon, Korea. |
| Pseudocode | Yes | Algorithm 1 TENSORSKETCH (Pham & Pagh, 2013), Algorithm 2 POLY-TENSORSKETCH, Algorithm 3 Greedy k-center, Algorithm 4 Coefficient approximation via coreset |
| Open Source Code | Yes | Our implementation and experiments are available at https://github.com/insuhan/polytensorsketch. |
| Open Datasets | Yes | For real-world kernels, we use segment and usps datasets. The datasets used in Section 4.1 and 4.2 are available at http: //www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ and http://archive.ics.uci.edu/. trained on CIFAR100 dataset (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | We run all experiments with 10 cross-validations and report the average of the classification error on the validation dataset. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models or cloud instances used for the experiments. |
| Software Dependencies | No | We use the open-source SVM package (LIBSVM) (Chang & Lin, 2011) and ADAM optimizer (Kingma & Ba, 2015), but no specific version numbers for these software dependencies are provided. |
| Experiment Setup | Yes | We set m = 10, r = 10 and k = 10 as the default configuration. We set m = 20 for the dimension of sketches and r = 3 for the degree of the polynomial. We set m = 20, d = 3, r = 3 and γ = 1. We first train the model for 300 epochs using ADAM opti-mizer (Kingma & Ba, 2015) with 0.0005 learning rate. |