7 citations · 7 across the 5 of their papers we have counts for
5 papers
Bayesian Networks for Path-Based Sensors: Gathering Information and Path Planning in Communication Denied Environments
Alkesh K. Srivastava, George P. Kontoudis, Donald Sofge +1
A "path-based sensor" produces a single observation along a continuous path. For example, a boolean path-based sensor returns a single "1" if an event of interest is detected at an…
Federated Gaussian Process Learning via Pseudo-Representations for Large-Scale Multi-Robot Systems
Sanket A. Salunkhe, George P. Kontoudis
Multi-robot systems require scalable and federated methods to model complex environments under computational and communication constraints. Gaussian Processes (GPs) offer robust pr…
Distributed Quantum Gaussian Processes for Multi-Agent Systems
Meet Gandhi, George P. Kontoudis
Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale real-world domains due to the limited ex…
Path-Based Sensors: Will the Knowledge of Correlation in Random Variables Accelerate Information Gathering?
Alkesh K. Srivastava, George P. Kontoudis, Donald Sofge +1
Effective communication is crucial for deploying robots in mission-specific tasks, but inadequate or unreliable communication can greatly reduce mission efficacy, for example in se…
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning
George P. Kontoudis, Daniel J. Stilwell
In this paper, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. To decentralize the implementation of GP t…