Sequential Inference for Gaussian Processes: A Signal Processing Perspective

Gaussian processes are being reexamined from a signal processing perspective, driven by the need for sequential inference in machine learning models. This shift is part of a broader methodological change in signal processing, which has been heavily influenced by the development of capable and efficient ML models. These models can represent complex, nonlinear relationships with high predictive accuracy, but adapting them often requires sequential inference, which poses distinct theoretical and practical challenges. Researchers are working to address these challenges, exploring new approaches to sequential inference for Gaussian processes that can support the development of more sophisticated signal processing systems. The implications of this work extend beyond the technical domain, as state-aligned threat activity raises the stakes for signal processing and machine learning, with geopolitical implications that go beyond the immediate target¹. This matters to practitioners because it highlights the need for more advanced and secure signal processing capabilities.