Identifiable Generative models for Missing Not at Random Data Imputation
Authors: Chao Ma, Cheng Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study the empirical performance of the proposed algorithm of Section 4 with both synthetic data (Section 6.1) and two real-world datasets with music recommendation (Section 6.2) and personalized education (Section 6.3) . |
| Researcher Affiliation | Collaboration | Chao Ma1,2 Cheng Zhang2 1University of Cambridge 2Microsoft Research Cambridge cm905@cam.ac.uk cheng.zhang@microsoft.com |
| Pseudocode | No | The paper describes the GINA algorithm textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is released at https://github.com/ microsoft/project-azua. |
| Open Datasets | Yes | We apply our models to recommendation systems on Yahoo! R3 dataset [30, 60] for user-song ratings... Finally, we apply our methods to the Eedi education dataset [61]... |
| Dataset Splits | Yes | In this experiment, we randomly split the data in a 90% train/ 10% test/ 10% validation ratio, and train our models on the response outcome data. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not specify the version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We use a 3-layer neural network (512, 512, 20) with ReLU activation for the encoder and decoder, with latent dimension 20. We use a 2-layer neural network for the missingness prediction network with dimension (512, 1). We train with batch size 128, for 500 epochs with Adam optimizer with learning rate 0.001. |