What’s a good imputation to predict with missing values?
Authors: Marine Le Morvan, Julie Josse, Erwan Scornet, Gael Varoquaux
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments confirm that joint imputation and regression through Neu Miss is better than various two step procedures in our experiments with finite number of samples. |
| Researcher Affiliation | Academia | 1 Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France 2 Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France 3 CMAP, UMR7641, Ecole Polytechnique, IP Paris, 91128 Palaiseau, France 4 Inria Sophia-Antipolis, Montpellier, France |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | The code for all experiments is available at https://github.com/marine LM/Impute_then_ Regress. |
| Open Datasets | No | The paper states: 'Data generation The data X 2 Rn d are generated according to a multivariate Gaussian distribution N(µ, ) where the mean is drawn from a standard Gaussian and the covariance is generated as = BB> + D.' This indicates simulated data, not a publicly available dataset. |
| Dataset Splits | Yes | The experiments use training sets of size n = 100 000 and validation and test sets of size n = 10 000. A validation set is used to choose MLPs depth (1, 2 or 5), width (1d, 5d or 10d), initial learning rate (ranging from 5.10 4 to 10 2) and weight decay (ranging from 10 6 to 10 3). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments were provided. |
| Software Dependencies | No | The paper mentions software like 'Py Torch' and 'Scikit-learn' but does not provide specific version numbers for these or any other ancillary software components. |
| Experiment Setup | Yes | A validation set is used to choose MLPs depth (1, 2 or 5), width (1d, 5d or 10d), initial learning rate (ranging from 5.10 4 to 10 2) and weight decay (ranging from 10 6 to 10 3). Adam is used with an adaptive learning rate: the learning rate is divided by 5 each time 10 consecutive epochs fail to decrease the training loss by at least 1e-4. Early stopping is triggered when the validation score does not improve by at least 1e-4 for 12 consecutive epochs. The batch size is set to 100, and Re LUs are used as activation functions. Finally for Neu Miss the depth is set to 20. |