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..
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning
Authors: Kanil Patel, William H. Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | I-Max is evaluated according to multiple performance metrics, including accuracy, ECE, Brier and NLL, and compared against benchmark calibration methods across multiple datasets and trained classifiers. |
| Researcher Affiliation | Collaboration | 1Bosch Center for Artificial Intelligence, Renningen, Germany 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany |
| Pseudocode | Yes | Algo. 1 in the appendix for pseudocode |
| Open Source Code | Yes | Code available at https://github.com/boschresearch/imax-calibration |
| Open Datasets | Yes | We evaluate post-hoc calibration methods on four benchmark datasets, i.e., Image Net (Deng et al., 2009), CIFAR 10/100 (Krizhevsky, 2009) and SVHN (Netzer et al., 2011) |
| Dataset Splits | Yes | We perform class-balanced random splits of the data test set, unless stated otherwise: the calibration and evaluation set sizes are both 25k for Image Net, and 5k for CIFAR10/100. |
| Hardware Specification | No | No explicit details on specific GPU models, CPU models, or other hardware specifications used for running experiments were found. |
| Software Dependencies | No | No specific software versions (e.g., Python 3.x, PyTorch 1.x) or library version numbers (e.g., Numpy X.Y, sklearn X.Y) are provided. |
| Experiment Setup | Yes | All scaling methods use the Adam optimizer with batch size 256 for CIFAR and 4096 for Image Net. The learning rate was set to 10 3 for temperature scaling Guo et al. (2017) and Platt scaling Platt (1999), 0.0001 for vector scaling Guo et al. (2017) and 10 5 for matrix scaling Guo et al. (2017). |