Concept erasure aims to remove a target concept from a representation while preserving the other information encoded in it. This is difficult because representations encode many concepts that are often correlated with the erasure target, so removing the target risks damaging them.
arXiv:2607. 03973v1 Announce Type: new Abstract: Concept erasure aims to remove a target concept from a representation while preserving the other information encoded in it.
BARRIER (Bounded Activation Regions for Robust Information Erasure) is a method for machine unlearning that confines parameter updates to a controlled activation space, allowing stronger erasure of targeted concepts while limiting collateral damage to other representations. By employing interval arithmetic, it derives a closed‑form bound on worst‑case representation changes in protected regions, which serves as a knowledge‑preservation objective. The approach is architecture‑agnostic, compatible with existing erasure objectives, and empirically shows competitive performance in both classification and generative tasks, with enhanced robustness against adversarial recovery attacks.
Reference-Guided Machine Unlearning (ReGUn) is a vision unlearning framework that prioritizes distributional indistinguishability over degradation-based heuristics. It uses disjoint held-out data to create a class-conditioned reference distribution for distillation, guiding forget samples toward non-member behavior without explicitly degrading predictions. Experiments across various architectures and datasets show that ReGUn achieves a competitive forgetting–utility trade-off and closely matches retrain-like membership inference behavior.
The paper introduces MUtE, a dual framework that simultaneously erases concept-specific information from representations and generates counterfactual mappings. By deriving new erasure functions based on optimal bounds, MUtE imposes a translational bias on counterfactual trajectories, aligning with geometric properties of concepts in language models. The authors demonstrate that this approach improves downstream algorithmic fairness and enables the generation of counterfactual texts.
The paper introduces DPVar, a fairness metric that measures the variance of the conditional mean prediction to directly optimize mean demographic parity in nonlinear regression. It presents two solvers—FBO, using a closed‑form hypergradient, and ITD, which differentiates through the conditional‑mean estimator updates—to handle the resulting functional bilevel problem. Experiments on 21 tabular datasets and the Communities & Crime data show that these methods achieve competitive or superior accuracy‑fairness trade‑offs compared to existing approaches.
arXiv:2607. 20073v1 Announce Type: new Abstract: AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases.
arXiv:2607. 09236v1 Announce Type: new Abstract: Machine unlearning in LLMs is the targeted removal of specific knowledge while preserving all other capabilities, critical for privacy and safety.
arXiv:2609.38929v1 Announce Type: new Abstract: Machine learning systems increasingly face the need to remove the influence of entire data domains, such as toxic language, harmful behavior, or topica...
NADS (Navigator‑Guided Data Selection) is a fine‑tuning framework for large language models that mitigates catastrophic forgetting when the original pretraining data are unavailable. It first trains a navigator model without preservation regularization to detect capability drift, then scores synthetic data candidates using the predictive divergence between the navigator and the pretrained model. Finally, it selects a diverse, utility‑aware subset of synthetic data and fine‑tunes while distilling pretrained behavior, achieving a better balance between new‑task performance and general capability preservation with reduced selection overhead.
The paper introduces a concept-guided poisoning unlearning framework that explicitly identifies semantic concepts most strongly linked to a target class or knowledge. For image classification, it uses a Post-hoc Concept Bottleneck Model to locate relevant image regions and creates poisoned samples; for large language models, it aggregates Integrated Gradients over multiple question responses to mask important content and generate poisoned training targets. Experiments across several datasets demonstrate effective unlearning while largely preserving model utility.
arXiv:2608.01008v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can memorize and disclose sensitive information, motivating machine unlearning to remove designated knowle...