Sijia Liu - CSE@MSU
OPTML - Home
The overarching goal of this project is to achieve a technological breakthrough in (multi-agent) cooperative lifelong learning theory and practice, so as to accomplish adversary-resilient, fast-adaptable, and communication/computation-efficient multimodal information fusion for distributed sensing systems in dynamic environments.
We aim to develop cross-layer distributed optimization (CLDO), a new framework where networked agents process data to solve a multi-level optimization problem, addressing needs like prediction accuracy, robustness, data-model efficiency, and quick environmental adaptation. This approach efficiently utilizes shared experiences, enabling fast adaptation in dynamic, multifaceted environments.
In this part, we aim to establish the CLDO framework for single-agent learning, focusing on two critical elements that enable robust and ongoing learning using multimodal data.
Sijia Liu - CSE@MSU
OPTML - Home