Kernel, RKHS, and Gaussian Processes
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Transcript of Kernel, RKHS, and Gaussian Processes
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Kernel, RKHS, and Gaussian Processes
Caution! No proof will be given.
Sungjoon Choi, SNU
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LeveragedGaussian Process Regression
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Leveraged Gaussian Processes
The original Gaussian process regression anchors positive training data.
The proposed leveraged Gaussian process regression anchors positive data while avoiding negative data.
Moreover, it is possible to vary the leverage of each training data from -‐1 to +1.
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Positive and Negative Motion Control Data for Autonomous
Navigation
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Training Phase
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Execution Phase
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Real World Experiments
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Leverage Optimization
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Leverage Optimization
• The key intuition behind the leverage optimization is that we cast the leverage optimization problem into a model selection problem in Gaussian process regression.
• However, the number of leverage parameters is equivalent to the number of training data.
• To handle this issue, we propose a sparse constrained leverage optimization where we assume that the majority of leverage parameters are +1.
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Leverage Optimization (v1)
Using proximal linearized minimization [1], the update rule for solving above optimization is
[1] J. Bolte, S. Sabach, and M. Teboulle, “Proximal alternating linearized minimization for nonconvex and nonsmooth problems,” Mathematical Programming, vol. 146, no. 1-2, pp. 459–494, 2014.
where the proximal mapping becomes soft-‐thresholding:
where �̅� = 𝛾 − 1.
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Leverage Optimization (v2)
We propose a new leverage optimization method by doubling the leverage parameters to positive and negative parts.
Furthermore, we assume multiple demonstrations are collected from one demonstrator.
By doing so, we have two major benefits:1. As the L1-‐norm regularizer is only on the positive parts of the leverages,
negative parts of the leverages can be optimized more accurately. 2. By doubling the variables, proximal mapping is no longer needed.
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Sensory Field Reconstruction
Sensory observations from correlated sensory fields
Leverage Optimization
Collect observations
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Sensory observations from correlated sensory fields
Proposed Method Previous Method Without Optimization
Sensory Field Reconstruction
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Autonomous Driving Experiments
Driving demonstrations with mixed qualities
Leverage Optimization
Collect observations
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Proposed Method
Previous Method
Autonomous Driving Experiments
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Proposed MethodWithout Optimization
Autonomous Driving Experiments
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