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 [1].
Distributed Conformal Prediction via Message Passing
Authors: Haifeng Wen, Hong Xing, Osvaldo Simeone
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we investigate the trade-offs between hyperparameter tuning requirements, communication overhead, coverage guarantees, and prediction set sizes across different network topologies. The code of our work is released on: https://github.com/Haifeng Wen/ Distributed-Conformal-Prediction. |
| Researcher Affiliation | Academia | 1Io T Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China 2Department of ECE, The Hong Kong University of Science and Technology, HK SAR 3Department of Engineering, King s College London, London, U.K.. |
| Pseudocode | Yes | The proposed Q-DCP is summarized in Algorithm 1. ... The proposed H-DCP is summarized in Algorithm 2. |
| Open Source Code | Yes | The code of our work is released on: https://github.com/Haifeng Wen/ Distributed-Conformal-Prediction. |
| Open Datasets | Yes | As in Lu et al. (2023), we first train a shared model f( ) using the Cifar100 training data set to generate the score function s( , ). Calibration data, obtained from the Cifar100 test data, is distributed in a non-i.i.d. manner among K = 20 devices... Path MNIST includes 9 classes and 107, 180 data samples in total (89, 996 for training, 10, 004 for validation, 7, 180 for test). |
| Dataset Splits | Yes | Path MNIST includes 9 classes and 107, 180 data samples in total (89, 996 for training, 10, 004 for validation, 7, 180 for test). |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, processor types, or memory amounts) were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | The hyperparameters for the Q-DCP loss (8) are chosen as follows. We set ฮบ = 2000 for the smooth function g( ) as suggested by Nkansah et al. (2021), and we choose ยต = 2000. Moreover, unless noted otherwise, in (8), we set s0 to be the average of the local score quantiles... For H-DCP, unless noted otherwise, we set the consensus rate to ฮท = 1, and the number of quantization levels to M = 1000. We set nk = 50 for all devices k V. |