Active Learning of Continuous-time Bayesian Networks through Interventions
Authors: Dominik Linzner, Heinz Koeppl
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our criterion on synthetic and realworld data. |
| Researcher Affiliation | Collaboration | Dominik Linzner 1 2 Heinz Koeppl 1 3... 1Department of Engineering and Information Technology, TU Darmstadt, Germany 2The Why Company Gmb H, Berlin, Germany 3Department of Biology, TU Darmstadt, Germany. |
| Pseudocode | Yes | Appendix A: Algorithm 1: (V)BHC driven experimental design |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We apply our method to the British Household Panel Survey (BHPS) (on Micro-social Change, 2003). This data-set has been collected yearly from 1991 to 2002, thus consisting of 11 time-points. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions "standard Matlab optimizers" but does not provide specific version numbers for Matlab or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | We set NS = 10 and the length of each trajectory fixed to be τ = 3 a.u. |