System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Authors: Linda Lu, Ayush Sekhari, Karthik Sridharan
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | B. Experimental Evaluation Our theoretical results provide guarantees for the worst case deletions. We experimentally verify our theory, and we demonstrate that in practice, Algorithm 1 can maintain small excess error beyond the core set deletion capacities proven in Theorem 4.1. Furthermore, Algorithm 1 is significantly more memory and computation time efficient compared to other unlearning methods for linear classification. |
| Researcher Affiliation | Academia | 1Cornell University 2Boston University. Correspondence to: Linda Lu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 System-Aware Unlearning Algorithm for Linear Classification using Selective Sampling; Algorithm 2 System-Aware Unlearning Algorithm for General Classification using Selective Sampling |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing code or a link to a code repository. |
| Open Datasets | Yes | Purchase Dataset: A binary classification dataset on item purchase data curated by Bourtoule et al. (2021) with 249,215 points in dimension d = 600. Margin Dataset: A synthetic binary classification dataset with 200,000 points in dimension d = 100 with a hard margin condition of γ = 0.1 ( u x > 0.1, x for some underlying u Rd). |
| Dataset Splits | No | The paper mentions comparing 'test accuracy' but does not provide specific details on how the datasets were split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific details about software dependencies with version numbers used in the experiments. |
| Experiment Setup | No | The experimental evaluation section describes the unlearning procedures and datasets used, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, or a concrete value for the sampling parameter κ) or detailed system-level training settings required for reproduction. |