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..
Boosted Generative Models
Authors: Aditya Grover, Stefano Ermon
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments are designed to demonstrate the superiority of the proposed boosting meta-algorithms on a wide variety of generative models and tasks. |
| Researcher Affiliation | Academia | Aditya Grover, Stefano Ermon Department of Computer Science Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Gen BGM(X = {xi}m i=1, T rounds) and Algorithm 2 Disc BGM(X = {xi}m i=1, T rounds, f-div) |
| Open Source Code | Yes | A reference implementation of the boosting meta-algorithms is available at https://github.com/ermongroup/bgm. |
| Open Datasets | Yes | binarized MNIST dataset of handwritten digits (Le Cun, Cortes, and Burges 2010). http://yann. lecun. com/exdb/mnist. benchmark datasets (Van Haaren and Davis 2012). |
| Dataset Splits | Yes | We observe 1, 000 training samples drawn independently from the data distribution... The test set contains 1, 000 samples from the same distribution. Model weights are chosen based on cross-validation. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or memory) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and 'variational autoencoders (VAE)' and 'convolutional neural network (CNN)' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The classifiers for Disc BGM are multi-layer perceptrons with two hidden layers of 100 units each and Re LU activations, trained to maximize f-divergences corresponding to the negative cross-entropy (NCE) and Hellinger distance (HD) using the Adam optimizer (Kingma and Welling 2014). We set T = 2 rounds for additive boosting and Gen BGM. VAE hidden layer architecture given in parenthesis (e.g., 200-100, 200-100-100, 300-100, 100-50). |