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Found 12 papers, 7 papers with code
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Despite tremendous advancements in large language models (LLMs) over recent years, a notably urgent challenge for their practical deployment is the phenomenon of hallucination, where the model fabricates facts and produces non-factual statements.
Towards Biologically Plausible and Private Gene Expression Data Generation
In this paper, we initiate a systematic analysis of how DP generative models perform in their natural application scenarios, specifically focusing on real-world gene expression data.
A Unified View of Differentially Private Deep Generative Modeling
The availability of rich and vast data sources has greatly advanced machine learning applications in various domains.
MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime
The potential of realistic and useful synthetic data is significant.
Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy
In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applications.
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
This work proposes FedLAP-DP, a novel privacy-preserving approach for federated learning.
Private Set Generation with Discriminative Information
Differentially private data generation techniques have become a promising solution to the data privacy challenge -- it enables sharing of data while complying with rigorous privacy guarantees, which is essential for scientific progress in sensitive domains.
RelaxLoss: Defending Membership Inference Attacks without Losing Utility
As a long-term threat to the privacy of training data, membership inference attacks (MIAs) emerge ubiquitously in machine learning models.
Responsible Disclosure of Generative Models Using Scalable Fingerprinting
Over the past years, deep generative models have achieved a new level of performance.
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
The wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains.
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements
In this paper, we perform a systematic investigation of backdoor attack on NLP models, and propose BadNL, a general NLP backdoor attack framework including novel attack methods.
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
In addition, we propose the first generic attack model that can be instantiated in a large range of settings and is applicable to various kinds of deep generative models.
