Variational Bayesian Unlearning
Authors: Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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. |