Temporal Logic Point Processes
Authors: Shuang Li, Lu Wang, Ruizhi Zhang, Xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrated the interpretability, prediction accuracy, and flexibility of our proposed temporal logic point processes (TLPP) on both synthetic (including Hawkes processes and self-correcting point process) and real data (including healthcare application about sepsis patients mortality prediction and finance application about credit card fraud event prediction). |
| Researcher Affiliation | Collaboration | 1Department of Statistics, Harvard University; 2Department of Computer Science, East China Normal University; 3Department of Statistics, University of Nebraska-Lincoln; 4Ant Group; 5H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology; 6School of Computational Science & Engineering, Georgia Institute of Technology. |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | MIMIC-III is an electronic health record dataset of patients admitted to the intensive care unit (Johnson et al., 2016). We used a credit card dataset from the UCSD-FICO Data Mining Contest"(FICO-UCSD., 2009) to detect fraud transactions. (FICO-UCSD., 2009. URL https://ebiquity.umbc.edu/blogger/2009/05/24/ucsd-data-mining-contest/.) |
| Dataset Splits | No | The paper mentions training data sizes (50, 500, and 4,000 patients) and test data sizes (100 patients), but does not explicitly describe a separate validation set or its 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. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We considered a one-layer RNN and LSTM with 128 hidden units. ... For our model, weights of logic rules were initialized as a small number, say, .001 (like the standard initialization for neural networks). To ensure non-negative weights, projected gradient descent was used in training. |