Proposes a vector-valued RKHS framework for Bayesian optimization with structured measurements, deriving concentration bounds and UCB-based regret guarantees that recover sublinear rates.
Operator-valued kernels for learning from functional response data.Journal of Machine Learning Research, 17(20):1–54
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DOODL learns a dictionary of spectral dynamics to approximate a manifold of related dynamical systems, enabling compact representations and improved operator estimation from short or partial trajectories.
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Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework
Proposes a vector-valued RKHS framework for Bayesian optimization with structured measurements, deriving concentration bounds and UCB-based regret guarantees that recover sublinear rates.
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Geometric Dictionary Learning of Dynamical Systems with Optimal Transport
DOODL learns a dictionary of spectral dynamics to approximate a manifold of related dynamical systems, enabling compact representations and improved operator estimation from short or partial trajectories.