ERC Advanced Grant · 2024
ODD-ML: Out-of-Distribution Deployable Machine Learning
ODD-ML addresses the open secret of machine learning (ML), which is that model deployment often fails. The problem arises because deployment contexts may differ from the data used to train ML models in unexpected ways. In an increasingly data-driven era, this severely impedes progress in ML-powered R&D and our ability to tackle societal grand challenges with existing ML tools. To solve this pervasive issue, I propose a radical alternative to current ML approaches, placing human experts at the core of iterative design-build-test-learn (DBTL) loops. My approach comprises the following major interlinked steps: 1. Re-conceptualize the deployment issue as a need for active learning from domain…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.