Nonparametric Embeddings of Sparse High-Order Interaction Events
Authors: Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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}. |