Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference
Authors: Sebastian Nowozin
ICLR 2018 | Venue PDF | LLM Run Details | Input Tokens: 20,022 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,824 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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}. |