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..
Nonparametric Embeddings of Sparse High-Order Interaction Events
Authors: Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For evaluation, we conducted simulations to demonstrate that our theoretical bounds can indeed match the actual sparsity ratio and capture the asymptotic trend. Hence they can provide a reasonable convergence rate estimate and characterize the behavior of the prior. We then tested our approach NESH on three real-world datasets. NESH achieves much better predictive performance than the existing methods that use Poisson tensor factorization, additional time steps, local time dependency windows and triggering kernels. |
| Researcher Affiliation | Academia | 1School of Computing, University of Utah 2Department of Mathematics, University of Utah 3Scientific Computing and Imaging (SCI) Institute, University of Utah. |
| Pseudocode | No | The paper describes the algorithm steps in text (Section 4 Algorithm) and uses mathematical equations, but there are no formally labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We then examined the predictive performance of NESH on the following real-world datasets. (1) Taobao (https://tianchi.aliyun.com/dataset/ data Detail?data Id=53),... (2) Crash (https://www.kaggle.com/ usdot/nhtsa-traffic-fatalities),... (3) Retail (https://tianchi.aliyun.com/dataset/ data Detail?data Id=37260), |
| Dataset Splits | No | We randomly split each dataset into 80% sequences for training, and the remaining 20% for test. The paper specifies training and test splits but does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. It only mentions implementing methods with PyTorch. |
| Software Dependencies | No | We implemented NESH, HP-Local, HP-TF and MGP-EF with Pytorch (Paszke et al., 2019), and the other methods with MATLAB. The software is mentioned, but specific version numbers are not provided for reproducibility. |
| Experiment Setup | Yes | Specifically, we introduce a small set of pseudo inputs Z = [z1, . . . , zh] for f( ), where h is far less than the dimension of f. We then define the pseudo outputs b = [f(z1), . . . , f(zh)] . ... We set the number of pseudo inputs to 100. We used the square exponential (SE) kernel and initialized the kernel parameters with 1. For HP-Local, the local window size was set to 50. For our method, we chose α from {0.5, 1.0, 1.5, 2.5, 3}. We conducted stochastic mini-batch optimization for all the methods, where the batch size was set to 100. We used ADAM (Kingma and Ba, 2014) algorithm, and the learning rate was tuned from {5 × 10−4, 10−3, 3 × 10−3, 5 × 10−3, 10−2}. We ran each method for 400 epochs, which is enough to converge. We randomly split each dataset into 80% sequences for training, and the remaining 20% for test. We varied R, the dimension of the embeddings, from {2, 5, 8, 10}. |