Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
Authors: Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate the benefits of Data-IQ on four real-world medical datasets. |
| Researcher Affiliation | Academia | Nabeel Seedat University of Cambridge ns741@cam.ac.uk Jonathan Crabbé University of Cambridge jc2133@cam.ac.uk Ioana Bica University of Oxford The Alan Turing Institute ioana.bica@eng.ox.ac.uk Mihaela van der Schaar University of Cambridge The Alan Turing Institute UCLA mv472@cam.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | See footnotes 2 and 3. 2 https://github.com/seedatnabeel/Data-IQ 3 https://github.com/vanderschaarlab/Data-IQ |
| Open Datasets | Yes | We conduct experiments on four real-world medical datasets... (1) Covid-19 dataset of Brazilian patients [38], (2) Prostate cancer datasets from both the US [39] and UK [40], (3) Support dataset of seriously ill hospitalized adults [41], (4) Fetal state dataset of cardiotocography [42]. |
| Dataset Splits | Yes | All models are trained to convergence, with early stopping on a validation set. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix B |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers in the main text or the ethics checklist. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix B, detailing all relevant information |