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..
Practical and Consistent Estimation of f-Divergences
Authors: Paul Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the behavior of our estimator empirically in both synthetic and real-data experiments, and discuss its direct implications for total correlation, entropy, and mutual information estimation. |
| Researcher Affiliation | Collaboration | Paul K. Rubenstein Max Planck Institute for Intelligent Systems, Tübingen & Machine Learning Group, University of Cambridge EMAIL Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya Tolstikhin Google Research, Brain Team, Zürich EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | A python notebook to reproduce all experiments is available at https://github.com/google-research/google-research/tree/master/f_divergence_estimation_ram_mc. |
| Open Datasets | Yes | We consider models pre-trained on the Celeb A dataset [25] |
| Dataset Splits | No | The paper mentions using a "test dataset" but does not specify training, validation, or test split percentages or sample counts for the CelebA dataset within the paper. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software versions for ancillary software. |
| Experiment Setup | Yes | We choose a setting in which QλZ parametrized by a scalar λ and PZ are both d-variate normal distributions for d ∈ {1, 4, 16}. ... We show the behaviour of RAM-MC with N ∈ {1, 500} and M=128... For the plug-in estimator, the densities ˆq(z) and ˆp(z) were estimated by kernel density estimation with 500 samples from QZ and PZ respectively... The divergence was then estimated via MC-sampling using 128 samples from QZ... RAM-MC is evaluated using N ∈ {20, 21, . . . , 214} and M ∈ {10, 103}. |