Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
Authors: Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we apply the not-MIWAE to problems with values MNAR: censoring in multivariate datasets, clipping in images and selection bias in recommender systems. Implementation details and a link to source code can be found in appendix A. |
| Researcher Affiliation | Academia | Niels Bruun Ipsen EMAIL Pierre-Alexandre Mattei EMAIL Jes Frellsen EMAIL Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark Universit e CΛote d Azur, Inria (Maasai team), Laboratoire J.A. Dieudonn e, UMR CNRS 7351, France |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at: https://github.com/nbip/not MIWAE |
| Open Datasets | Yes | We compare different imputation techniques on datasets from the UCI database (Dua & Graff, 2017), street view house numbers dataset (SVHN, Netzer et al., 2011) and The Yahoo! R3 dataset (webscope.sandbox.yahoo.com). |
| Dataset Splits | No | While the Yahoo! R3 dataset describes separate training and test sets, the paper does not provide specific percentages, counts, or a methodology for data splitting (including validation splits) for any of the datasets (UCI, SVHN, Yahoo! R3) that would be needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only general software frameworks. |
| Software Dependencies | No | The paper mentions using 'TensorFlow probability (Dillon et al., 2017) and the Adam optimizer (Kingma & Ba, 2014)' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The encoder and decoder consist of two hidden layers with 128 units and tanh activation functions. ... The size of the latent space is set to p β 1, K = 20 importance samples were used during training and a batch size of 16 was used for 100k iterations. ... K = 5 importance samples were used during training and a batch size of 64 was used for 1M iterations. ... We use K = 20 importance samples during training, ReLU activations, a batch size of 100 and train for 10k iterations. |