When Priors Backfire: On the Vulnerability of Unlearnable Examples to Pretraining
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Zhihao, Xu, Gezheng, Cai, Jiale, Fang, Ruiyi, Wu, Di, Lao, Qicheng, Ling, Charles, Wang, Boyu |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression
by: Pu, Ruizhi, et al.
Published: (2024)
by: Pu, Ruizhi, et al.
Published: (2024)
Intersectional Unfairness Discovery
by: Xu, Gezheng, et al.
Published: (2024)
by: Xu, Gezheng, et al.
Published: (2024)
Provably Unlearnable Data Examples
by: Wang, Derui, et al.
Published: (2024)
by: Wang, Derui, et al.
Published: (2024)
Unlearnable Examples For Time Series
by: Jiang, Yujing, et al.
Published: (2024)
by: Jiang, Yujing, et al.
Published: (2024)
Game-Theoretic Unlearnable Example Generator
by: Liu, Shuang, et al.
Published: (2024)
by: Liu, Shuang, et al.
Published: (2024)
Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise
by: Liu, Yixin, et al.
Published: (2023)
by: Liu, Yixin, et al.
Published: (2023)
ZETA: Leveraging Z-order Curves for Efficient Top-k Attention
by: Zeng, Qiuhao, et al.
Published: (2025)
by: Zeng, Qiuhao, et al.
Published: (2025)
ARMOR: Shielding Unlearnable Examples against Data Augmentation
by: Gong, Xueluan, et al.
Published: (2025)
by: Gong, Xueluan, et al.
Published: (2025)
Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
by: Wang, Bo, et al.
Published: (2026)
by: Wang, Bo, et al.
Published: (2026)
Stabilized Fine-Tuning with LoRA in Federated Learning: Mitigating the Side Effect of Client Size and Rank via the Scaling Factor
by: Huang, Jiayu, et al.
Published: (2026)
by: Huang, Jiayu, et al.
Published: (2026)
Scale-up Unlearnable Examples Learning with High-Performance Computing
by: Zhu, Yanfan, et al.
Published: (2025)
by: Zhu, Yanfan, et al.
Published: (2025)
Generalizing across Temporal Domains with Koopman Operators
by: Zeng, Qiuhao, et al.
Published: (2024)
by: Zeng, Qiuhao, et al.
Published: (2024)
Why Do Unlearnable Examples Work: A Novel Perspective of Mutual Information
by: Zhu, Yifan, et al.
Published: (2026)
by: Zhu, Yifan, et al.
Published: (2026)
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining
by: Zhang, Ruiyi, et al.
Published: (2024)
by: Zhang, Ruiyi, et al.
Published: (2024)
Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
by: Yu, Yi, et al.
Published: (2024)
by: Yu, Yi, et al.
Published: (2024)
Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learning
by: Yang, Yi, et al.
Published: (2026)
by: Yang, Yi, et al.
Published: (2026)
On the Benefits of Attribute-Driven Graph Domain Adaptation
by: Fang, Ruiyi, et al.
Published: (2025)
by: Fang, Ruiyi, et al.
Published: (2025)
A Survey on Unlearnable Data
by: Li, Jiahao, et al.
Published: (2025)
by: Li, Jiahao, et al.
Published: (2025)
When Stronger Triggers Backfire: A High-Dimensional Theory of Backdoor Attacks
by: Flynn, Donald, et al.
Published: (2026)
by: Flynn, Donald, et al.
Published: (2026)
Homophily Enhanced Graph Domain Adaptation
by: Fang, Ruiyi, et al.
Published: (2025)
by: Fang, Ruiyi, et al.
Published: (2025)
UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation
by: Sun, Ye, et al.
Published: (2024)
by: Sun, Ye, et al.
Published: (2024)
Entropy-Guided Dynamic Tokens for Graph-LLM Alignment in Molecular Understanding
by: Jing, Zihao, et al.
Published: (2026)
by: Jing, Zihao, et al.
Published: (2026)
SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads
by: Lao, Jiale, et al.
Published: (2025)
by: Lao, Jiale, et al.
Published: (2025)
The Unlearnability Phenomenon in RLVR for Language Models
by: Chen, Yulin, et al.
Published: (2026)
by: Chen, Yulin, et al.
Published: (2026)
Nonlinear Transformations Against Unlearnable Datasets
by: Hapuarachchi, Thushari, et al.
Published: (2024)
by: Hapuarachchi, Thushari, et al.
Published: (2024)
UTOPIA: Unlearnable Tabular Data via Decoupled Shortcut Embedding
by: He, Jiaming, et al.
Published: (2026)
by: He, Jiaming, et al.
Published: (2026)
FlowX: Towards Explainable Graph Neural Networks via Message Flows
by: Gui, Shurui, et al.
Published: (2022)
by: Gui, Shurui, et al.
Published: (2022)
Unlearnable phases of matter
by: Kumar, Tarun Advaith, et al.
Published: (2026)
by: Kumar, Tarun Advaith, et al.
Published: (2026)
When Incentives Backfire, Data Stops Being Human
by: Santy, Sebastin, et al.
Published: (2025)
by: Santy, Sebastin, et al.
Published: (2025)
When and How Human Curation Backfires: Preference Alignment under Multi-Model Self-Consuming Loop
by: Zhang, Yang, et al.
Published: (2026)
by: Zhang, Yang, et al.
Published: (2026)
Medical Unlearnable Examples: Securing Medical Data from Unauthorized Training via Sparsity-Aware Local Masking
by: Sun, Weixiang, et al.
Published: (2024)
by: Sun, Weixiang, et al.
Published: (2024)
GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered
by: Lao, Jiale, et al.
Published: (2026)
by: Lao, Jiale, et al.
Published: (2026)
How Far Are We from True Unlearnability?
by: Ye, Kai, et al.
Published: (2025)
by: Ye, Kai, et al.
Published: (2025)
Scaling-Aware Adapter for Structure-Grounded LLM Reasoning
by: Jing, Zihao, et al.
Published: (2026)
by: Jing, Zihao, et al.
Published: (2026)
When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification
by: Zhao, Jiale, et al.
Published: (2026)
by: Zhao, Jiale, et al.
Published: (2026)
SoK: Unlearnability and Unlearning for Model Dememorization
by: Zhang, Mengying, et al.
Published: (2026)
by: Zhang, Mengying, et al.
Published: (2026)
Conda: Column-Normalized Adam for Training Large Language Models Faster
by: Wang, Junjie, et al.
Published: (2025)
by: Wang, Junjie, et al.
Published: (2025)
DistilLock: Safeguarding LLMs from Unauthorized Knowledge Distillation on the Edge
by: Mohanty, Asmita, et al.
Published: (2025)
by: Mohanty, Asmita, et al.
Published: (2025)
The Finetuner's Fallacy: When to Pretrain with Your Finetuning Data
by: Baek, Christina, et al.
Published: (2026)
by: Baek, Christina, et al.
Published: (2026)
Learning from Convolution-based Unlearnable Datasets
by: Kim, Dohyun, et al.
Published: (2024)
by: Kim, Dohyun, et al.
Published: (2024)
Similar Items
-
Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression
by: Pu, Ruizhi, et al.
Published: (2024) -
Intersectional Unfairness Discovery
by: Xu, Gezheng, et al.
Published: (2024) -
Provably Unlearnable Data Examples
by: Wang, Derui, et al.
Published: (2024) -
Unlearnable Examples For Time Series
by: Jiang, Yujing, et al.
Published: (2024) -
Game-Theoretic Unlearnable Example Generator
by: Liu, Shuang, et al.
Published: (2024)