Adaptive-lambda Subtracted Importance Sampled Scores in Machine Unlearning for DDPMs and VAEs

TL;DR

This paper introduces Adaptive-lambda SISS, a dynamic approach for machine unlearning in generative models that improves the balance between forgetting specific data and maintaining overall model quality.

Contribution

The paper proposes a novel adaptive lambda mechanism for SISS, enhancing unlearning effectiveness and extending the approach to score-based models with new hybrid and reinforcement learning methods.

Findings

Adaptive-lambda SISS outperforms static-lambda SISS in unlearning tasks.

The method achieves better class removal and preserves generation quality.

Experiments on MNIST demonstrate significant improvements.

Abstract

Machine Unlearning is essential for large generative models (VAEs, DDPMs) to comply with the right to be forgotten and prevent undesired content generation without costly retraining. Existing approaches, such as Static-lambda SISS for diffusion models, rely on a fixed mixing weight lambda, which is suboptimal because the required unlearning strength varies across samples and training stages. We propose Adaptive-lambda SISS, a principled extension that turns lambda into a latent variable dynamically inferred at each training step. A lightweight inference network parameterizes an adaptive posterior over lambda, conditioned on contextual features derived from the instantaneous SISS loss terms (retain/forget losses and their gradients). This enables joint optimization of the diffusion model and the lambda-inference mechanism via a variational objective, yielding significantly better trade-offs. We further extend the adaptive-lambda principle to score-based unlearning and introduce a multi-class variant of Score Forgetting Distillation. In addition, we present two new directions: (i) a hybrid objective combining the data-free efficiency of Score Forgetting Distillation with the direct gradient control of SISS, and (ii) a Reinforcement Learning formulation that treats unlearning as a sequential decision process, learning an optimal policy over a state space defined by the model's current memory of the forget set. Experiments on an augmented MNIST benchmark show that Adaptive-lambda SISS substantially outperforms the original static-lambda SISS, achieving stronger removal of forgotten classes while better preserving generation quality on the retain set.