Differentiable and Transportable Structure Learning
Authors: Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela Van Der Schaar
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we empirically validate D-Struct with respect to edge accuracy and structural Hamming distance in a variety of settings. |
| Researcher Affiliation | Academia | 1DAMTP, University of Cambridge, UK 2UCLA, CA, USA 3The Alan Turing Institute, UK. |
| Pseudocode | Yes | Algorithm 1: Outer-loop of dual ascent procedure for D-Struct(NOTEARS-MLP) and Algorithm 2: training step for DStruct(NOTEARS-MLP) cfr. Alg. 1 |
| Open Source Code | Yes | Please find our online code repository at: https://github.com/jeroenbe/d-struct or https://github.com/vanderschaarlab Our code is based on code provided by Zheng et al. [36], and we annotated our code where we used their implementation. |
| Open Datasets | No | The paper describes generating synthetic data based on 'Erdos-Reny ı (ER) graphs' and 'Scale-Free graphs' with structural equations as in Zheng et al. [36]. While the generation method is cited, the specific datasets used in the experiments are not provided via a direct link, DOI, or named repository, nor are they described as a pre-existing public dataset. |
| Dataset Splits | No | The paper mentions drawing 'samples' and splitting them for D-Struct's internal mechanism (e.g., 'split the samples into two equal-sized subsets' for transportability evaluation), but does not specify standard training, validation, and test splits for model evaluation or reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Python', 'PyTorch', and the 'L-BFGS-B optimizer' being used, and that their code is based on Zheng et al. [36]'s implementation, but it does not specify version numbers for any of these software components or libraries. |
| Experiment Setup | Yes | Specifically, we let K distinct DSFs learn a DAG from one of the K datasets, agnostic from each other. If the chosen DSF requires hyperparameters (such as λ1,2 and ρ in eq. (4)), we have to also include these in D-Struct s set of required hyperparameters. While it is possible to set different hyperparameter values for each of the DSFs separately (which is potentially helpful when there is a lot of variety in the K distinct datasets), we fix these across DSFs in light of simplicity. A discussion on D-Struct s hyperparameters can be found in Appendix A.1. We set our hyperparameters to those which yielded best performance (deduced from Fig. 7 for α, and K = 3 when not varied over as this yielded the most stable results overall). |