Federated learning is a new learning paradigm for extracting knowledge f...
This work, for the first time, introduces two constant factor approximat...
Understanding the COVID-19 vaccine hesitancy, such as who and why, is ve...
Federated learning (FL) was originally regarded as a framework for
colla...
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and ...
Although the effects of the social norm on mitigating misinformation are...
Recent development in the field of explainable artificial intelligence (...
Temporal Graph Neural Network (TGNN) has been receiving a lot of attenti...
In the last few years, many explanation methods based on the perturbatio...
Despite recent studies on understanding deep neural networks (DNNs), the...
Temporal graph neural networks (TGNNs) have been widely used for modelin...
In this paper, we show that the process of continually learning new task...
With the rapid growth of threats, sophistication and diversity in the ma...
5G radio access network (RAN) with network slicing methodology plays a k...
Bilevel optimization has been applied to a wide variety of machine learn...
Despite the great potential of Federated Learning (FL) in large-scale
di...
Quantum annealing (QA) that encodes optimization problems into Hamiltoni...
In this work, we study the problem of monotone non-submodular maximizati...
Federated learning is known to be vulnerable to security and privacy iss...
Knowledge Distillation (KD) has been considered as a key solution in mod...
In this paper, we focus on preserving differential privacy (DP) in conti...
This paper studies a Group Influence with Minimum cost which aims to fin...
In this paper, we study a novel problem, Minimum Robust Multi-Submodular...
In Graph Neural Networks (GNNs), the graph structure is incorporated int...
Studying on networked systems, in which a communication between nodes is...
Graph mining plays a pivotal role across a number of disciplines, and a
...
Since 2016, sharding has become an auspicious solution to tackle the
sca...
A major challenge in blockchain sharding protocols is that more than 95
...
Although the iterative double auction has been widely used in many diffe...
Cost-aware Targeted Viral Marketing (CTVM), a generalization of Influenc...
In this work, we study the Submodular Cost Submodular Cover problem, whi...
Due to high complexity of many modern machine learning models such as de...
In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM)...
In this paper, we aim to develop a novel mechanism to preserve different...
In this paper, we explore the partitioning attacks on the Bitcoin networ...
This paper focuses on network resilience to perturbation of edge weight....
In this paper, a novel economic approach, based on the framework of cont...
The optimization of submodular functions on the integer lattice has rece...