arXiv:2605.23470v1 Announce Type: new Abstract: Predicting how a dynamical unit evolves over time - how an individual ages, an epidemic spreads, or a physical system degrades - typically requires dense longitudinal tracking. When only extremely sparse or entirely cross-sectional data is available, inferring individualized, continuous-time trajectories is fundamentally ill-posed. Existing methods force a strict compromise: sequence models (e.g. latent ODEs) require dense longitudinal data, while cross-sectional methods (e.g. optimal transport, flow matching-based) map aggregate populations, los
The proliferation of sparse, real-world datasets in various domains, combined with advancements in AI and probabilistic modeling, necessitates new methods for extracting dynamic insights from incomplete information.
This research addresses a fundamental limitation in AI's ability to model continuous, individualized change from common data types, potentially unlocking new applications in healthcare, economics, and social sciences.
The capacity to infer individual trajectories from limited 'snapshot' data could transform how we understand and predict complex systems, moving beyond aggregate population views.
- · Healthcare diagnostics
- · Econometric modeling
- · Epidemiology
- · Personalized AI applications
- · Traditional statistical modeling relying on dense longitudinal data
- · AI models limited to abundant, time-series data
Improved predictive models for individual-level outcomes based on routinely collected, sparse data.
Development of new AI agents and monitoring systems that can infer complex states and predict needs from non-continuous observations.
Enhanced AI-driven personalization across industries, from tailored educational programs to custom product recommendations, derived from fragmented user interactions.
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Read at arXiv cs.LG