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..
Decision-Making with Auto-Encoding Variational Bayes
Authors: Romain Lopez, Pierre Boyeau, Nir Yosef, Michael Jordan, Jeffrey Regier
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing. In this challenging instance of multiple hypothesis testing, our proposed approach surpasses the current state of the art. |
| Researcher Affiliation | Academia | Romain Lopez1, Pierre Boyeau1, Nir Yosef1,2,3, Michael I. Jordan1,4, and Jeffrey Regier5 1 Department of Electrical Engineering and Computer Sciences, University of California, Berkeley 2 Chan-Zuckerberg Biohub, San Francisco 3 Ragon Institute of MGH, MIT and Harvard 4 Department of Statistics, University of California, Berkeley 5 Department of Statistics, University of Michigan |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode blocks or algorithms. |
| Open Source Code | Yes | Our code is available at http://github.com/Pierre Boyeau/decision-making-vaes |
| Open Datasets | Yes | We consider the MNIST dataset, which includes features x for each of the images of handwritten digits and a label c. [...] We split the MNIST dataset evenly between training and test datasets. For the labels 0 to 8, we use a total of 1,500 labeled examples. |
| Dataset Splits | No | The paper mentions splitting the MNIST dataset into training and test sets but does not specify the exact percentages or counts for these splits, nor does it explicitly detail a separate validation set split or how these splits can be reproduced. |
| Hardware Specification | Yes | In the p PCA experiment, training a single VAE takes 12 seconds, while step one and two of our method together take 53 seconds (on a machine with a single NVIDIA Ge Force RTX 2070 GPU). |
| Software Dependencies | No | The paper mentions software components like "neural network" and the "Gumbel-softmax trick", but it does not specify any programming languages, libraries, or solvers with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | Unless stated otherwise, we use 30 particles per iteration for training the models (as in [19]), 10,000 samples for reporting log-likelihood proxies, and 200 particles for making decisions. All results are averaged across five random initializations of the neural network weights. [...] AIS is computationally intensive, so we used 500 steps and 100 samples from the prior to keep the runtime manageable. |