machine unlearning

FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness AAAI 2026 Forgetting by Pruning: Data Deletion in Join Cardinality Estimation AAAI 2026 Forget What Has Seen: Selective Concept Unlearning in Segmentation Foundation Models AAAI 2026 From Chaos to Clarity: A Knowledge Graph-Driven Audit Dataset Generation Framework for LLM Unlearning AAAI 2026 From Narrow Unlearning to Emergent Misalignment in LLMs ACL 2026 Fully Decentralized Certified Unlearning CVPR 2026 GDFA: Geometry-Driven Federated Unlearning with Directional Task Vector Alignment CVPR 2026 Grokked Models are Better Unlearners UAI 2026 Injection, Attack and Erasure: Revocable Backdoor Attacks via Machine Unlearning AAAI 2026 JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification ACL 2026 Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation ACL 2026 Knowledge Control for Responsible Generative AI: Bridging Academia, Industry, and Society ACL 2026 Large Language Model Unlearning for Source Code AAAI 2026 LOTUS: Evolving Multimodal Unlearning via Hyperbolic Entailment and Lorentz Transport ACL 2026 Machine Unlearning via Adaptive Gradient Reweighting and Multi-stage Objective Optimization CVPR 2026 Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning ACL 2026 Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem ACL 2026 Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs EACL 2026 Not All Tokens Are Meant to Be Forgotten AAAI 2026 Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models AAAI 2026 On the Misalignment Between Data Learnability and Forgettability in Machine Unlearning AAAI 2026 PAGE: A Unified Approach for Federated Graph Unlearning AAAI 2026 PeriUn: Enhancing Unlearning by Selectively Forgetting Peripheral Samples AAAI 2026 pH-Strips for Selective Forgetting: A Blunt but Fast Diagnostic Baseline for Machine Unlearning CVPR 2026 POUR: A Provably Optimal Method for Unlearning Representation via Neural Collapse CVPR 2026