ICML 2025PastGenerative models
ICML 2025 Workshop on Machine Unlearning for Generative AI
MUGen @ ICML 2025
- Submission deadline
- May 24, 2025, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (36)
Fetched from OpenReview (v2) on 2026-06-10.
Align-then-Unlearn: Embedding Alignment for LLM Unlearning
An Empirical Exploration of Continual Unlearning for Image Generation
Ascent Fails to Forget
Automating Evaluation of Diffusion Model Unlearning with (Vision-) Language Model World Knowledge
Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images
Breaking Weight Entanglement: Machine Unlearning with Nonlinearity
ContinualFlow: Learning and Unlearning with Neural Flow Matching
Data-Unlearn-Bench: Making Evaluating Data Unlearning Easy
Distributional Unlearning: Forgetting Distributions, Not Just Samples
Do Not Mimic My Voice: Speaker Identity Unlearning for Zero-Shot Text-to-Speech
DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoning
Embarrassingly Efficient Unlearning with SVD
Erased but Not Forgotten: How Backdoors Compromise Concept Erasure
Evaluating Deep Unlearning in Large Language Models
Gauss-Newton Unlearning for the LLM Era
GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detection
Koopman Autoencoders Learn Neural Representation Dynamics
Learning-Time Encoding Shapes Unlearning in LLMs
Machine Unlearning under Overparameterization
Model Unlearning via Sparse Autoencoder Subspace Guided Projections
Noisy But Forgotten: LLM Unlearning are Robust against Perturbed Data in the Wild
On the Fragility of Latent Knowledge: Layer-wise Influence under Unlearning in Large Language Model
OVERT: A Benchmark for Over-Refusal Evaluation on Text-to-Image Models
Reference-Specific Unlearning Metrics Can Hide the Truth: A Reality Check
Rethinking Backdoor Unlearning Through Linear Task Decomposition
Rethinking Evaluation Metrics for Machine Unlearning
Rethinking Unlearning for Large Reasoning Models
Reveal-or-Obscure: A Differentially Private Sampling Algorithm
Selective Knowledge Unlearning via Self-Distillation with Auxiliary Forget-Set Model
Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning
Understanding Machine Unlearning Through the Lens of Mode Connectivity
Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs
Unleashing Uncertainty: Efficient Machine Unlearning for Generative AI
Unveiling Concept Attribution in Diffusion Models
WaterDrum: Watermarking for Data-centric Unlearning Metric
When to Forget? Complexity Trade-offs in Machine Unlearning