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 [1].
Cardinality-Regularized Hawkes-Granger Model
Authors: Tsuyoshi Ide, Georgios Kollias, Dzung Phan, Naoki Abe
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the proposed framework with two real use-cases, one from the power grid and the other from the cloud data center management domain. |
| Researcher Affiliation | Industry | Tsuyoshi Idé IBM Research, T. J. Watson Research Center EMAIL Georgios Kollias IBM Research, T. J. Watson Research Center EMAIL Dzung T. Phan IBM Research, T. J. Watson Research Center EMAIL Naoki Abe IBM Research, T. J. Watson Research Center EMAIL |
| Pseudocode | Yes | Algorithm summary Algorithm 1 summarizes L0Hawkes, the proposed algorithm, which is used as part of the iterative MM procedure in Eq. (13). |
| Open Source Code | No | The paper does not provide a direct link to a code repository for the proposed method nor explicitly state that the code is publicly available. |
| Open Datasets | Yes | We generated two synthetic multivariate event datasets, Sparse5 and Dense10, with a standard point process simulator tick [4]. We obtained failure event data (Grid) of power grid from U.S. Department of Energy [29]. |
| Dataset Splits | No | The paper discusses cross-validation for parameter determination ('These parameters should eventually be cross-validated with independent episodes of event data, or ground through causality data.') but does not provide specific percentages or counts for training, validation, and test splits for the datasets used in experiments. |
| Hardware Specification | Yes | The mean computational time was (46, 881, 382) seconds per one parameter set for (L0Hawkes, c MLP, c LSTM), respectively, on a laptop PC (i7 CPU, 32GB memory, Quadro P3200 GPU). |
| Software Dependencies | No | The paper mentions using 'tick' [4], which is a Python library, but does not specify a version number for it or for any other software dependencies crucial for reproducibility. |
| Experiment Setup | Yes | We chose νA, νβ, νµ to be 0.1 and tested τ = 0.5, 1, 2. We grid-searched the model parameters based on AIC to get 5 (10−3, 10−4, 10−4) for (νµ, νβ, µA) and (1, 1) for (τ, ϵ). |