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..
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference
Authors: Sebastian Nowozin
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now empirically validate our key claims regarding the JVI method: 1. JVI produces better estimates of the marginal likelihood by reducing bias, even for small K; and 2. Higher-order bias reduction is more effective than lower-order bias reduction; To this end we will use variational autoencoders trained on MNIST. |
| Researcher Affiliation | Industry | Sebastian Nowozin Machine Intelligence and Perception Microsoft Research, Cambridge, UK EMAIL |
| Pseudocode | Yes | Algorithm 1 Computing ΛLJ,m K , the jackknife variational inference estimator |
| Open Source Code | Yes | 1The implementation is available at https://github.com/Microsoft/ jackknife-variational-inference |
| Open Datasets | Yes | Our setup is purposely identical to the setup of Tomczak & Welling (2016), where we use the dynamically binarized MNIST data set of Salakhutdinov & Murray (2008). |
| Dataset Splits | Yes | We train on the ο¬rst 50,000 training images, using 10,000 images for validation. |
| Hardware Specification | Yes | All our models are implemented using Chainer (Tokui et al., 2015) and run on a NVidia Titan X. |
| Software Dependencies | No | The paper mentions 'Chainer (Tokui et al., 2015)' but does not specify a version number for this software dependency. |
| Experiment Setup | Yes | Hyperparameters are the batch size in {1024, 4096} and the SGD step size in {0.1, 0.05, 0.01, 0.005, 0.001}. |