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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring
Authors: Gunwoong Park, Garvesh Raskutti
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide both theoretical guarantees and simulation results for both small and large-scale DAGs. |
| Researcher Affiliation | Academia | Gunwoong Park Department of Statistics University of Wisconsin-Madison Madison, WI 53706 EMAIL Garvesh Raskutti Department of Statistics Department of Computer Science Wisconsin Institute for Discovery, Optimization Group University of Wisconsin-Madison Madison, WI 53706 EMAIL |
| Pseudocode | Yes | Algorithm 1: Over Dispersion Scoring (ODS) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper states, 'The simulation study was conducted using 50 realizations of a p-node random Poisson DAG that was generated as follows.' It does not refer to a publicly available or open dataset, nor does it provide access to the generated data. |
| Dataset Splits | No | The paper describes using simulated data and sets parameters like 'c0 = 0.005' and 'λ = 0.1' for algorithms, but it does not specify explicit training, validation, or test dataset splits in the traditional sense for a fixed dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions algorithms like GLMLasso [17], MMPC [15], HITON [13], PC [3], MMHC [15], GES [18], and SC [16], but it does not specify version numbers for these software components or libraries. |
| Experiment Setup | Yes | The simulation study was conducted using 50 realizations of a p-node random Poisson DAG that was generated as follows. The gj(.) functions for the general Poisson DAG model (1) was chosen using the standard GLM link function (i.e.gj(XPa(j)) = exp(θj + Pk Pa(j) θjk Xk)) resulting in the GLM DAG model (2). In all results presented (θjk) parameters were chosen uniformly at random in the range θjk [ 1, 0.7]... In our experiments, we always set the thresholding constant c0 = 0.005... 3 different algorithms are used for Step 1): GLMLasso [17] where we choose λ = 0.1; MMPC [15] with α = 0.005; and HITON [13] again with α = 0.005 and an oracle where the edges for the true moralized graph is used. |