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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Importance Weighting and Variational Inference
Authors: Justin Domke, Daniel R. Sheldon
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,675 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,341 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 | All the following experiments compare E-IWVI using student T distributions to IWVI using Gaussians. |
| Researcher Affiliation | Academia | 1 College of Information and Computer Sciences, University of Massachusetts Amherst 2 Department of Computer Science, Mount Holyoke College |
| Pseudocode | Yes | Algorithm 1 A generative process for q M(z1:M) |
| Open Source Code | No | The paper does not provide an unambiguous statement or a link to open-source code for the methodology described. |
| Open Datasets | Yes | From top: Madelon (d = 500) Sonar (d = 60), Mushrooms (d = 112). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | Yes | Stan Development Team. Modeling language user s guide and reference manual, version 2.17.0 |
| Experiment Setup | Yes | On these, we used a fixed set of 10, 000 M random inputs to T and optimized using batch L-BFGS, avoiding heuristic tuning of a learning rate sequence. Finally, we considered a (non-conjugate) logistic regression model with a Cauchy prior with a scale of 10, using stochastic gradient descent with various step sizes. |