46 citations · 157 across the 49 of their papers we have counts for
14 papers · 1 filter
Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
Yan Zhou, Kevin Hamlen, Michael De Lucia +5
Machine learning-based intrusion detection requires complex models to capture patterns in high-dimensional, noisy, and class-imbalanced raw network traffic, yet deploying such mode…
Regret Bounds for Reinforcement Learning from Multi-Source Imperfect Preferences
Ming Shi, Yingbin Liang, Ness B. Shroff +1
Reinforcement learning from human feedback (RLHF) replaces hard-to-specify rewards with pairwise trajectory preferences, yet regret-oriented theory often assumes that preference la…
FlowSymm: Physics Aware, Symmetry Preserving Graph Attention for Network Flow Completion
Ege Demirci, Francesco Bullo, Ananthram Swami +1
Recovering missing flows on the edges of a network, while exactly respecting local conservation laws, is a fundamental inverse problem that arises in many systems such as transport…
Mitigating Participation Imbalance Bias in Asynchronous Federated Learning
Xiangyu Chang, Manyi Yao, Srikanth V. Krishnamurthy +5
In Asynchronous Federated Learning (AFL), the central server immediately updates the global model with each arriving client's contribution. As a result, clients perform their local…
Fully Distributed Online Training of Graph Neural Networks in Networked Systems
Rostyslav Olshevskyi, Zhongyuan Zhao, Kevin Chan +3
Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power gr…
Unsupervised Joint -node Graph Representations with Compositional Energy-Based Models
Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami +1
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in…