
Consistency and Regression with Laplacian regularization in Reproducing Kernel Hilbert Space
This note explains a way to look at reproducing kernel Hilbert space for...
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Solving Chance Constrained Optimization under NonParametric Uncertainty Through Hilbert Space Embedding
In this paper, we present an efficient algorithm for solving a class of ...
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Adaptive Smoothing Path Integral Control
In Path Integral control problems a representation of an optimally contr...
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Koopman Linearization for DataDriven Batch State Estimation of ControlAffine Systems
We present the Koopman State Estimator (KoopSE), a framework for modelf...
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Optimal Rates for Spectralregularized Algorithms with LeastSquares Regression over Hilbert Spaces
In this paper, we study regression problems over a separable Hilbert spa...
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On the Optimality of KernelEmbedding Based GoodnessofFit Tests
The reproducing kernel Hilbert space (RKHS) embedding of distributions o...
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Reactive Navigation under NonParametric Uncertainty through Hilbert Space Embedding of Probabilistic Velocity Obstacles
The probabilistic velocity obstacle (PVO) extends the concept of velocit...
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Path Integral Control by Reproducing Kernel Hilbert Space Embedding
We present an embedding of stochastic optimal control problems, of the so called path integral form, into reproducing kernel Hilbert spaces. Using consistent, sample based estimates of the embedding leads to a model free, nonparametric approach for calculation of an approximate solution to the control problem. This formulation admits a decomposition of the problem into an invariant and task dependent component. Consequently, we make much more efficient use of the sample data compared to previous sample based approaches in this domain, e.g., by allowing sample reuse across tasks. Numerical examples on test problems, which illustrate the sample efficiency, are provided.
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