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Found 13 papers, 2 papers with code
UNIDOOR: A Universal Framework for Action-Level Backdoor Attacks in Deep Reinforcement Learning
Deep reinforcement learning (DRL) is widely applied to safety-critical decision-making scenarios.
Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
By adopting the graph attention transfer method, GRAPHNAD can effectively align the intermediate-layer attention representations of the backdoored model with that of the teacher model, forcing the backdoor neurons to transform into benign ones.
AEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image Models
As text-to-image (T2I) models continue to advance and gain widespread adoption, their associated safety issues are becoming increasingly prominent.
Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents
To enhance the reliability of LLM-based applications, a range of research has emerged to assess and mitigate these risks from different perspectives.
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
The main obstacle is that existing gradient estimators have suboptimal tail properties, resulting in a superfluous factor of $d$ in the union bound.
Learning with User-Level Local Differential Privacy
Under the central model, user-level DP is strictly stronger than the item-level one.
Enhancing Learning with Label Differential Privacy by Vector Approximation
Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public.
Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning
In this study, we propose a novel client selection strategy designed to emulate the performance achieved with full client participation.
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
We provide a theoretical analysis of our approach, which gives the noise strength needed for privacy protection, as well as the bound of mean squared error.
An Inversion-based Measure of Memorization for Diffusion Models
The past few years have witnessed substantial advances in image generation powered by diffusion models.
Pacos: Modeling Users' Interpretable and Context-Dependent Choices in Preference Reversals
Choice problems refer to selecting the best choices from several items, and learning users' preferences in choice problems is of great significance in understanding the decision making mechanisms and providing personalized services.
Probe: Learning Users' Personalized Projection Bias in Intertemporal Choices
In this work, we specifically focus on two commonly observed biases: projection bias and the reference-point effect.
A homogenized damping model for the propagation of elastic wave in a porous solid
This paper develops an averaging technique based on the combination of the eigenfunction expansion method and the collaboration method to investigate the multiple scattering effect of the SH wave propagation in a porous medium.
Numerical Analysis Materials Science Statistical Mechanics Numerical Analysis 14J60 (Primary) 14F05, 14J26 (Secondary) F.2.2; I.2.7
