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..
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier
Authors: Yuling Yao, Justin Domke
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate an automated implementation using neural networks and statistically-inspired features, and validate the method with numerical and real data experiments. |
| Researcher Affiliation | Collaboration | Yuling Yao Flatiron Institute, New York, NY 10010. EMAIL Justin Domke University of Massachusetts, Amherst, MA 01002. EMAIL |
| Pseudocode | Yes | Algorithm 1: Proposed method: Discriminative calibration |
| Open Source Code | Yes | We share Jax implementation of our binary and multiclass classifier calibration in Github2. https://github.com/yao-yl/Disc Calibration |
| Open Datasets | Yes | Next, we apply our calibration to three models from the SBI benchmark [23]: the simple likelihood complex posterior (SLCP), the Gaussian linear, and the Gaussian mixture model. ... [23] Lueckmann, J.-M., Boelts, J., Greenberg, D., Goncalves, P., and Macke, J. (2021). Benchmarking simulation-based inference. In International Conference on Artificial Intelligence and Statistics. |
| Dataset Splits | Yes | Randomly split the LS classification examples (t, ϕ) into training and validation sets (all L examples for a given i go to either training or validation); ... We tune the weight of the decay term by a 5 fold cross-validation in the training set on a fixed grid {0.1, 0.01, 0.001, 0.0001}. |
| Hardware Specification | No | The paper mentions 'one classification run with roughly one million examples took roughly two hour cpu time on a local laptop' (Appendix B.2), but it does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | We share the python and Jax implementation of our binary and multiclass classifier calibration in https://github.com/yao-yl/Disc Calibration. No specific version numbers for Python or Jax are provided. |
| Experiment Setup | Yes | In the MLP training we include a standard L2 weight decay (i.e., training loss function = cross entropy loss + tuning weight L2 penalization). We tune the weight of the decay term by a 5 fold cross-validation in the training set on a fixed grid {0.1, 0.01, 0.001, 0.0001}. ... we use one-hidden-layer MLP with 64 nodes to parameterize the classifier with the form (11), with additional pre-learned features such as log q(θ|y) added as linear features. |