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..
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration
Authors: Yuanjie Shi, Subhankar Ghosh, Taha Belkhouja, Jana Doppa, Yan Yan
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on multiple real-world datasets demonstrate that RC3P achieves class-wise coverage and 26.25% reduction in prediction set sizes on average. |
| Researcher Affiliation | Academia | Yuanjie Shi Washington State University Subhankar Ghosh Washington State University Taha Belkhouja Washington State University Janardhan Rao Doppa Washington State University Yan Yan Washington State University |
| Pseudocode | Yes | Algorithm 1 RC3P Method for Class-Conditional CP |
| Open Source Code | Yes | The code is available at https://github.com/Yuanjie Sh/RC3P. |
| Open Datasets | Yes | We consider four datasets: CIFAR-10, CIFAR-100 [34], mini-Image Net [57], and Food-101 [8] by using the standard training and validation split. |
| Dataset Splits | Yes | Randomly split data into train Dtr and calibration Dcal and train the classifier f on Dtr.we randomly split the original (balanced) validation set into calibration samples and testing samples. |
| Hardware Specification | No | The paper mentions 'Res Net-20 [27]' as the main architecture and 'LDAM proposed by [10]' as the training algorithm, but does not specify any hardware details such as GPU/CPU models or memory used for experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch [44]' but does not provide a specific version number for it or any other software components. |
| Experiment Setup | Yes | To handle imbalanced data, we employ the training algorithm LDAM proposed by [10] that assigns different margins to classes... where all models are trained with 200 epochs... learning rate 0.1, momentum 0.9, and weight decay 2e-4 for 200 epochs and 50 epochs. The batch size is 128. For Food-101, the batch size is 256... We set α = 0.1 as our main experiment setting and also report other experiment results of different α values (See Appendix C.7). |