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Nicholas Tagliapietra

2 accepted papers

2026

Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis

AAAI 2026technical

Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics typically either discretize time ---leading to poor performance on irregularly sampled data--- or ignore the underlying

Cited by 0SourcePDFScholar
2024

Exact Inference for Continuous-Time Gaussian Process Dynamics

AAAI 2024technical

Many physical systems can be described as a continuous-time dynamical system. In practice, the true system is often unknown and has to be learned from measurement data. Since data is typically collected in discrete time, e.g. by sensors, most methods in Gaussian process (GP) dynamics model learning…

Cited by 2SourcePDFScholar