Order-Dependent Event Models for Agent Interactions
Authors: Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that the proposed model fits various event datasets involving single as well as multiple agents better than baseline models. We also illustrate potentially useful insights from our proposed model for an analyst during the discovery process through analysis on a real-world political event dataset. ... We conduct an experiment to evaluate how well the proposed model fits the afore-mentioned datasets. ... Table 1 compares the log likelihood evaluated on test sets across models. |
| Researcher Affiliation | Industry | Debarun Bhattacharjya , Tian Gao and Dharmashankar Subramanian Research AI, IBM T. J. Watson Research Center {debarunb, tgao, dharmash}@us.ibm.com |
| Pseudocode | Yes | Algorithm 1 Ordinal Summary Statistics 1: procedure SUMMARYSTATS(event label X, parents U, window w X, masking function φ( ), dataset D) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | ICEWS [O Brien, 2010]. ... Mimic-II [Saeed et al., 2011]. ... Diabetes [Frank and Asuncion, 2010]. ... Linked In [Xu et al., 2017]. |
| Dataset Splits | Yes | Each dataset is split into three sets: train (70%), dev (15%) and test (15%), only retaining event labels that are common to all three splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions models like PGEM, OGEM, and NHP but does not specify the software libraries or their version numbers used for implementation or experimentation (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | Hyper-parameter choices for OGEM and the baselines are as follows: OGEM: We search over a default rate hyper-parameter grid of λ0 = {0.001, 0.005, 0.01, 0.05, 0.1}. Window hyper-parameter grids are dataset specific, chosen as: ICEWS: w X={1, 3, 7, 10, 15, 30, 60} (days) X Mimic: w X={0.1, 0.2, 0.5, 1, 1.5, 2, 5} (years) X Diabetes: w X={0.01, 0.05, 0.1, 0.5, 1, 5} (days) X Linked In: w X={2, 5, 7, 10, 15, 20} (years) X PGEM: The closest baseline is the proximal GEM, which allows different windows for different parents but does not distinguish between orders of causal events. We deploy the learning approach in Bhattacharjya et al. [2018], which also identifies windows using a heuristic. We use left limiting parameter ϵ = 0.001 and default rate λ0 as the only hyper-parameter with the same grid as OGEM. NHP: Primarily just for reference, we also learn a neural Hawkes process [Mei and Eisner, 2017], a state-of-the-art neural architecture for event models. Neural networks are expected to do much better than fully parametric ones on the model fitting task due to the large number of parameters. NHP does not however learn a graphical model and is less interpretable than the other models considered, making it less useful for discovery. For NHP, the only hyperparameter is the number of epochs for training. For all models, the optimal hyper-parameter setting is chosen by training models under various settings using the train set and finding the best performing setting on the dev set. The optimal trained model is then evaluated on the test set. |