When to Forget? Complexity Trade-offs in Machine Unlearning
Authors: Martin Van Waerebeke, Marco Lorenzi, Giovanni Neglia, Kevin Scaman
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments: We investigate the global landscape of the unlearning complexity ratio as a function of key factors, including the accepted excess risk threshold e and the unlearning budget (ϵ, δ), which are jointly quantified by the constant κϵ,δ. 5.1. Experimental Setting: The goal of the experiment section is to validate the theoretical analysis presented in Section 4 by comparing the performance of unlearning and retraining on both real and synthetic functions and datasets. 5.2. Experiments on Synthetic Data. 5.3. Experiments on Real Data. 5.4. Experimental Results: Figure 2 illustrates the empirical unlearning complexity ratio. |
| Researcher Affiliation | Academia | 1INRIA Paris 2INRIA Sophia Antipolis. Correspondence to: Martin Van Waerebeke <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Iterative (Un)Learning Algorithm Algorithm 2 Noise and Fine-Tune Unlearning Algorithm |
| Open Source Code | No | The paper does not contain any explicit statements about releasing code or links to source code repositories for the methodology described. |
| Open Datasets | Yes | 5.1. Experimental Setting: We aim to learn linear regression models in Rd (with even d). We perform experiments both on synthetic worst-case functions, as analysed in our theory, and on the Digit dataset of handwritten digits, which is a subset of the larger dataset proposed in Alpaydin and Alimoglu (1996). Alpaydin, E. and Alimoglu, F. (1996). Pen-Based Recognition of Handwritten Digits. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5MG6K. |
| Dataset Splits | Yes | In every experiment, the retain and forget are obtained through the random splitting of the dataset into two parts of respective sizes n rf n and rf n . We consider rf = 10 2. |
| Hardware Specification | No | The paper mentions experiments being performed but does not provide specific details about the hardware used (e.g., GPU models, CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using 'stochastic gradient descent' and 'the standard SGD optimizer' but does not provide specific version numbers for any software libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | 5.3. Experiments on Real Data: For the real data, the experimental process is simpler as we optimize a standard cross-entropy loss with L2 regularization. For various values of κϵ,δ and e, we measure T U e and T S e in a more realistic machine learning setting, with decaying learning rate, batch size of 64, and averaging the results over 50 runs. The learning rate is initialised at 10 2 and multiplied by 0.6 every 1000 epoch. Each experiment is repeated 50 times and the results are then averaged. We used a batch-size of 64 and trained until the threshold e was reached, for every chosen value of κϵ,δ. The optimized used is the standard SGD optimizer without acceleration. |