Nonstationary Sparse Spectral Permanental Process
Authors: Zicheng Sun, Yixuan Zhang, Zenan Ling, Xuhui Fan, Feng Zhou
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of our approach, particularly in scenarios with pronounced data nonstationarity. Additionally, ablation studies are conducted to provide insights into the impact of various hyperparameters on model performance. |
| Researcher Affiliation | Academia | Zicheng Sun1 , Yixuan Zhang2 , Zenan Ling3, Xuhui Fan4 Feng Zhou1,5 1Center for Applied Statistics and School of Statistics, Renmin University of China 2School of Statistics and Data Science, Southeast University 3School of EIC, Huazhong University of Science and Technology 4School of Computing, Macquarie University 5Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing |
| Pseudocode | Yes | E Pseudocode The pseudocode of our inference algorithm is provided in Algorithm 1. Algorithm 1: Inference for (D)NSSPP |
| Open Source Code | Yes | Code is publicly available at https://github.com/SZC20/DNSSPP. |
| Open Datasets | Yes | Coal Mining Disaster [13]: This is a 1-dimensional dataset containing 191 incidents that occurred between March 15, 1875, and March 22, 1962. ... Redwoods [27]: This is a 2-dimensional dataset... Porto Taxi [21]: This is a large 2-dimensional dataset... |
| Dataset Splits | Yes | For each intensity, we simulate ten datasets and use each dataset alternately as the training set and the remaining ones as the test sets. For each dataset, we randomly partition the data into training set and test set of approximately equal size. |
| Hardware Specification | Yes | We perform all experiments using the server with two GPUs (NVIDIA TITAN V with 12GB memory), two CPUs (each with 8 cores, Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz), and 251GB memory. |
| Software Dependencies | No | The paper does not explicitly state specific version numbers for software dependencies such as programming languages or libraries. |
| Experiment Setup | Yes | For NSSPP, the number of frequencies (network width) is set to 50. For DNSSPP, we experimented with three different configurations: DNSSPP-[50,30], DNSSPP-[100,50], and DNSSPP-[30,50,30]. Each number represents the width of the corresponding network layer. We investigate DNSSPP s performance with varying network sizes by adjusting width and depth on nonstationary synthetic data. We conduct experiments on the nonstationary synthetic data to investigate the coordination of epoch and learning rate. |