Adaptive-lambda Subtracted Importance Sampled Scores in Machine Unlearning for DDPMs and VAEs
Fuente:
arXiv
Saved in:
| Main Authors: | Dini, MohammadParsa, Jafari, Human |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Autospeculation
by: Hu, Hengyuan, et al.
Published: (2025)
by: Hu, Hengyuan, et al.
Published: (2025)
Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
by: Cadet, Xavier F., et al.
Published: (2024)
by: Cadet, Xavier F., et al.
Published: (2024)
The Value of Covariance Matching in Gaussian DDPMs and the Lanczos Sampler
by: Akhtar, Md Sahil, et al.
Published: (2026)
by: Akhtar, Md Sahil, et al.
Published: (2026)
EEG-Based Consumer Behaviour Prediction: An Exploration from Classical Machine Learning to Graph Neural Networks
by: Afshar, Mohammad Parsa, et al.
Published: (2025)
by: Afshar, Mohammad Parsa, et al.
Published: (2025)
AIS: Adaptive Importance Sampling for Quantized RL
by: Zhou, Jiajun, et al.
Published: (2026)
by: Zhou, Jiajun, et al.
Published: (2026)
From SHAP Scores to Feature Importance Scores
by: Letoffe, Olivier, et al.
Published: (2024)
by: Letoffe, Olivier, et al.
Published: (2024)
Verifying Machine Unlearning with Explainable AI
by: Vidal, Àlex Pujol, et al.
Published: (2024)
by: Vidal, Àlex Pujol, et al.
Published: (2024)
AdaProb: Efficient Machine Unlearning via Adaptive Probability
by: Zhao, Zihao, et al.
Published: (2024)
by: Zhao, Zihao, et al.
Published: (2024)
The Unseen Threat: Residual Knowledge in Machine Unlearning under Perturbed Samples
by: Hsu, Hsiang, et al.
Published: (2026)
by: Hsu, Hsiang, et al.
Published: (2026)
Adversarial Robustness of VAEs across Intersectional Subgroups
by: Ramanaik, Chethan Krishnamurthy, et al.
Published: (2024)
by: Ramanaik, Chethan Krishnamurthy, et al.
Published: (2024)
Disentangling Granularity: An Implicit Inductive Bias in Factorized VAEs
by: Chen, Zihao, et al.
Published: (2025)
by: Chen, Zihao, et al.
Published: (2025)
Unity by Diversity: Improved Representation Learning in Multimodal VAEs
by: Sutter, Thomas M., et al.
Published: (2024)
by: Sutter, Thomas M., et al.
Published: (2024)
Agentic Unlearning: When LLM Agent Meets Machine Unlearning
by: Wang, Bin, et al.
Published: (2026)
by: Wang, Bin, et al.
Published: (2026)
Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases
by: Han, Ling, et al.
Published: (2024)
by: Han, Ling, et al.
Published: (2024)
Machine Unlearning under Overparameterization
by: Block, Jacob L., et al.
Published: (2025)
by: Block, Jacob L., et al.
Published: (2025)
Group-robust Machine Unlearning
by: De Min, Thomas, et al.
Published: (2025)
by: De Min, Thomas, et al.
Published: (2025)
Soft Weighted Machine Unlearning
by: Qiao, Xinbao, et al.
Published: (2025)
by: Qiao, Xinbao, et al.
Published: (2025)
Machine Unlearning: Solutions and Challenges
by: Xu, Jie, et al.
Published: (2023)
by: Xu, Jie, et al.
Published: (2023)
A Survey of Machine Unlearning
by: Nguyen, Thanh Tam, et al.
Published: (2022)
by: Nguyen, Thanh Tam, et al.
Published: (2022)
Machine Unlearning in Contrastive Learning
by: Wang, Zixin, et al.
Published: (2024)
by: Wang, Zixin, et al.
Published: (2024)
Importance Sampling for Nonlinear Models
by: Rajmohan, Prakash Palanivelu, et al.
Published: (2025)
by: Rajmohan, Prakash Palanivelu, et al.
Published: (2025)
ScoreMix: Synthetic Data Generation by Score Composition in Diffusion Models Improves Recognition
by: Rahimi, Parsa, et al.
Published: (2025)
by: Rahimi, Parsa, et al.
Published: (2025)
A Testable Certificate for Constant Collapse in Teacher-Guided VAEs
by: Zhang, Zegu, et al.
Published: (2026)
by: Zhang, Zegu, et al.
Published: (2026)
Score-Regularized Joint Sampling with Importance Weights for Flow Matching
by: Liu, Xinshuang, et al.
Published: (2025)
by: Liu, Xinshuang, et al.
Published: (2025)
SIMU: Selective Influence Machine Unlearning
by: Agarwal, Anu, et al.
Published: (2025)
by: Agarwal, Anu, et al.
Published: (2025)
Data Augmentation Improves Machine Unlearning
by: Falcao, Andreza M. C., et al.
Published: (2025)
by: Falcao, Andreza M. C., et al.
Published: (2025)
Debiasing Machine Unlearning with Counterfactual Examples
by: Chen, Ziheng, et al.
Published: (2024)
by: Chen, Ziheng, et al.
Published: (2024)
An Information Theoretic Approach to Machine Unlearning
by: Foster, Jack, et al.
Published: (2024)
by: Foster, Jack, et al.
Published: (2024)
Towards Reliable Testing of Machine Unlearning
by: Mazhar, Anna, et al.
Published: (2026)
by: Mazhar, Anna, et al.
Published: (2026)
SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers
by: Jafari, Aref, et al.
Published: (2025)
by: Jafari, Aref, et al.
Published: (2025)
Efficient Machine Unlearning via Influence Approximation
by: Liu, Jiawei, et al.
Published: (2025)
by: Liu, Jiawei, et al.
Published: (2025)
Feature-Selective Representation Misdirection for Machine Unlearning
by: Chen, Taozhao, et al.
Published: (2025)
by: Chen, Taozhao, et al.
Published: (2025)
Machine Unlearning of Traffic State Estimation and Prediction
by: Wang, Xin, et al.
Published: (2025)
by: Wang, Xin, et al.
Published: (2025)
Machine Unlearning in Low-Dimensional Feature Subspace
by: Fang, Kun, et al.
Published: (2026)
by: Fang, Kun, et al.
Published: (2026)
Machine Unlearning via Null Space Calibration
by: Chen, Huiqiang, et al.
Published: (2024)
by: Chen, Huiqiang, et al.
Published: (2024)
On the Limitations and Prospects of Machine Unlearning for Generative AI
by: Zhou, Shiji, et al.
Published: (2024)
by: Zhou, Shiji, et al.
Published: (2024)
A More Practical Approach to Machine Unlearning
by: Zagardo, David
Published: (2024)
by: Zagardo, David
Published: (2024)
Unlearning Clients, Features and Samples in Vertical Federated Learning
by: Varshney, Ayush K., et al.
Published: (2025)
by: Varshney, Ayush K., et al.
Published: (2025)
Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss
by: Lee, Jungi, et al.
Published: (2026)
by: Lee, Jungi, et al.
Published: (2026)
Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference
by: Oshima, Yuta, et al.
Published: (2024)
by: Oshima, Yuta, et al.
Published: (2024)
Similar Items
-
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Autospeculation
by: Hu, Hengyuan, et al.
Published: (2025) -
Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
by: Cadet, Xavier F., et al.
Published: (2024) -
The Value of Covariance Matching in Gaussian DDPMs and the Lanczos Sampler
by: Akhtar, Md Sahil, et al.
Published: (2026) -
EEG-Based Consumer Behaviour Prediction: An Exploration from Classical Machine Learning to Graph Neural Networks
by: Afshar, Mohammad Parsa, et al.
Published: (2025) -
AIS: Adaptive Importance Sampling for Quantized RL
by: Zhou, Jiajun, et al.
Published: (2026)