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..
Variational Bayesian Unlearning
Authors: Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate our unlearning methods on Bayesian models such as sparse Gaussian process and logistic regression using synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Dept. of Computer Science, National University of Singapore, Republic of Singapore Dept. of Electrical Engineering and Computer Science, MIT, USA |
| Pseudocode | No | The paper describes algorithms and methods in prose and mathematical formulations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We empirically demonstrate our unlearning methods on Bayesian models such as sparse Gaussian process and logistic regression using synthetic and real-world datasets. Further experimental results on Bayesian linear regression and with a bimodal posterior belief are reported in Appendices C and D, respectively. ... The banknote authentication dataset [10] of size |D| = 1372 is partitioned into erased data of size |De| = 412 and remaining data of size |Dr| = 960. ... The fashion MNIST dataset of size |D| = 60000 (28 28 images of fashion items in 10 classes) is partitioned into erased data of size |De| = 10000 and remaining data of size |Dr| = 50000. ... This section illustrates the scalability of unlearning to the massive airline dataset of 2 million flights [15]. |
| Dataset Splits | Yes | The banknote authentication dataset [10] of size |D| = 1372 is partitioned into erased data of size |De| = 412 and remaining data of size |Dr| = 960. ... The fashion MNIST dataset of size |D| = 60000 (28 28 images of fashion items in 10 classes) is partitioned into erased data of size |De| = 10000 and remaining data of size |Dr| = 50000. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like RMSProp, normalizing flows with MADE architecture, and Gaussians, but does not provide specific version numbers for these or other libraries. |
| Experiment Setup | Yes | We use RMSProp as the SGA algorithm with a learning rate of 10 4. ... We initialize qu(θ|Dr; λ) at q(θ|D) for achieving empirically faster convergence. |