Search Results for author:
Found 10 papers, 5 papers with code
Private Wasserstein Distance with Random Noises
Wasserstein distance is a principle measure of data divergence from a distributional standpoint.
VGMShield: Mitigating Misuse of Video Generative Models
Together with fake video detection and tracing, our multi-faceted set of solutions can effectively mitigate misuse of video generative models.
Teach Large Language Models to Forget Privacy
Large Language Models (LLMs) have proven powerful, but the risk of privacy leakage remains a significant concern.
Data Valuation and Detections in Federated Learning
In scenarios involving numerous data clients within FL, it is often the case that only a subset of clients and datasets are pertinent to a specific learning task, while others might have either a negative or negligible impact on the model training process.
Split-and-Denoise: Protect large language model inference with local differential privacy
Large Language Models (LLMs) shows powerful capability in natural language understanding by capturing hidden semantics in vector space.
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years.
GFlowCausal: Generative Flow Networks for Causal Discovery
Causal discovery aims to uncover causal structure among a set of variables.
PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems
Then, the paper proposes a new privacy-preserving framework based on homomorphic encryption, Privacy-Preserving Multi-View Matrix Factorization (PrivMVMF), to enhance user data privacy protection in federated recommender systems.
Graph Decipher: A transparent dual-attention graph neural network to understand the message-passing mechanism for the node classification
To improve functionality, we propose a new transparent network called Graph Decipher to investigate the message-passing mechanism by prioritizing in two main components: the graph structure and node attributes, at the graph, feature, and global levels on a graph under the node classification task.
Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network
Dynamic graph neural networks have been widely used in modeling and representation learning of graph structure data.
