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..
Differentially Private Distributed Bayesian Linear Regression with MCMC
Authors: Baris Alparslan, Sinan Yıldırım, Ilker Birbil
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical results on both real and simulated data, which demonstrate that the proposed algorithms provide well-rounded estimation and prediction. We present several numerical evaluations of the proposed methods, MCMC-normal X, MCMC-fixed S, and Bayes-fixed S-fast, with simulated and real data. |
| Researcher Affiliation | Academia | 1Faculty of Engineering and Sciences, Sabancı University, Turkey 2Amsterdam Business School, University of Amsterdam, The Netherlands. |
| Pseudocode | Yes | Algorithm 1 MCMC-normal X one iteration, Algorithm 2 MCMC-fixed S one iteration, Algorithm 3 Bayes-fixed S-fast |
| Open Source Code | Yes | Code for the experiments: Link to the code and the data for the experiments: https://github.com/ sinanyildirim/Bayesian_DP_dist_LR.git. |
| Open Datasets | Yes | For the real data case, we use four different data sets from the UCI Machine Learning Repository. power plant energy 7655 4 view link bike sharing 13904 14 view link air quality 7486 12 view link 3droad 347900 3 view link |
| Dataset Splits | No | For prediction, we took 80% of the data for training and the rest for testing. |
| Hardware Specification | Yes | The algorithms were run in MATLAB 2021b on an Apple M1 chip with 8 cores and 16 GB LPDDR4 memory. |
| Software Dependencies | Yes | The algorithms were run in MATLAB 2021b on an Apple M1 chip with 8 cores and 16 GB LPDDR4 memory. |
| Experiment Setup | Yes | For inference, we used the same Λ, κ as above and a = 20, b = 0.5, m = 0d 1, C = b/(a 1)Id. All the MCMC algorithms were run for 10^4 iterations. For each (J, ϵ) pair, we ran each method for 50 times (each with different noisy observations) to obtain average performances. |