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Found 5 papers, 2 papers with code
Approximation of optimization problems with constraints through kernel Sum-Of-Squares
Assuming further that the functions appearing in the problem are smooth, focusing on pointwise equality constraints enables the use of scattering inequalities to mitigate the curse of dimensionality in sampling the constraints.
Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM
We also show that Expectation Maximization (EM) can always formally be written as a mirror descent.
Kernel Stein Discrepancy Descent
We investigate the properties of its Wasserstein gradient flow to approximate a target probability distribution $\pi$ on $\mathbb{R}^d$, known up to a normalization constant.
Handling Hard Affine SDP Shape Constraints in RKHSs
The modular nature of the proposed approach allows to simultaneously handle multiple shape constraints, and to tighten an infinite number of constraints into finitely many.
Hard Shape-Constrained Kernel Machines
Shape constraints (such as non-negativity, monotonicity, convexity) play a central role in a large number of applications, as they usually improve performance for small sample size and help interpretability.
