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Found 22 papers, 2 papers with code
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs
Prior algorithms in both paradigms achieve the optimal group regret.
On Collaboration in Distributed Parameter Estimation with Resource Constraints
We study sensor/agent data collection and collaboration policies for parameter estimation, accounting for resource constraints and correlation between observations collected by distinct sensors/agents.
On-Demand Communication for Asynchronous Multi-Agent Bandits
We propose ODC, an on-demand communication protocol that tailors the communication of each pair of agents based on their empirical pull times.
Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning
In the context of an efficient network traffic engineering process where the network continuously measures a new traffic matrix and updates the set of paths in the network, an automated process is required to quickly and efficiently identify when and what set of paths should be used.
Quickest Change Detection in the Presence of Transient Adversarial Attacks
We study a monitoring system in which the distributions of sensors' observations change from a nominal distribution to an abnormal distribution in response to an adversary's presence.
To Collaborate or Not in Distributed Statistical Estimation with Resource Constraints?
We study how the amount of correlation between observations collected by distinct sensors/learners affects data collection and collaboration strategies by analyzing Fisher information and the Cramer-Rao bound.
Distributed Bandits with Heterogeneous Agents
The goal for each agent is to find its optimal local arm, and agents can cooperate by sharing their observations with others.
Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback
This paper studies a cooperative multi-armed bandit problem with $M$ agents cooperating together to solve the same instance of a $K$-armed stochastic bandit problem with the goal of maximizing the cumulative reward of agents.
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems
We show that in the presence of defense mechanisms deployed by the communicating parties, our attack performs significantly better compared to existing attacks against DNN-based wireless systems.
Adversarial Attack
Cryptography and Security
Bosonic Random Walk Networks for Graph Learning
The development of Graph Neural Networks (GNNs) has led to great progress in machine learning on graph-structured data.
Identification of Additive Link Metrics: Proof of Selected Theorems
This is a technical report, containing all the theorem proofs in the following two papers: (1) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Identifiability of Link Metrics Based on End-to-end Path Measurements," in ACM IMC, 2013.
Networking and Internet Architecture
Node Failure Localization: Theorem Proof
This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7. 3 Performance, 2015.
Networking and Internet Architecture
Filtered Manifold Alignment
Domain adaptation is an essential task in transfer learning to leverage data in one domain to bolster learning in another domain.
Network Anomaly Detection based on Tensor Decomposition
The problem of detecting anomalies in time series from network measurements has been widely studied and is a topic of fundamental importance.
Decentralized gradient methods: does topology matter?
Consensus-based distributed optimization methods have recently been advocated as alternatives to parameter server and ring all-reduce paradigms for large scale training of machine learning models.
Learning Features of Network Structures Using Graphlets
In this paper, we explore the role of \emph{graphlets} in network classification for both static and temporal networks.
Network Classification in Temporal Networks Using Motifs
Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world.
Sparse Diffusion-Convolutional Neural Networks
The predictive power and overall computational efficiency of Diffusion-convolutional neural networks make them an attractive choice for node classification tasks.
Selective Harvesting over Networks
We find that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as an ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect.
Diffusion-Convolutional Neural Networks
We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data.
Ranked #7 on
Node Classification
on PubMed (0.1%)
Learning to Generate Networks
We investigate the problem of learning to generate complex networks from data.
Estimating and Sampling Graphs with Multidimensional Random Walks
We show that the proposed sampling method, which we call Frontier sampling, exhibits all of the nice sampling properties of a regular random walk.
Data Structures and Algorithms Networking and Internet Architecture G.3
