no code implementations • 18 Oct 2022 • Benjamin Biggs, James McMahon, Philip Baldoni, Daniel J. Stilwell
We provide theoretical bounds on the worst case performance of the greedy algorithm in seeking to maximize a normalized, monotone, but not necessarily submodular objective function under a simple partition matroid constraint.
no code implementations • 6 Mar 2022 • 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.
no code implementations • 26 Jul 2021 • Michael E. Kepler, Alec Koppel, Amrit Singh Bedi, Daniel J. Stilwell
Gaussian processes (GPs) are a well-known nonparametric Bayesian inference technique, but they suffer from scalability problems for large sample sizes, and their performance can degrade for non-stationary or spatially heterogeneous data.
no code implementations • 23 Mar 2021 • Jia Guo, Michael E. Kepler, Sai Tej Paruchuri, Haoran Wang, Andrew J. Kurdila, Daniel J. Stilwell
Approximations of the evolution of the ideal local estimate $\hat{g}^i_t$ of agent $i$ is constructed solely using observations made by agent $i$ on a fine time scale.