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Optimal Learning Rates for Regularized Least-Squares with a Fourier Capacity Condition
We derive minimax adaptive rates for a new, broad class of Tikhonov-regularized learning problems in Hilbert scales under general source conditions.
Dynamic Pricing and Demand Learning on a Large Network of Products: A PAC-Bayesian Approach
The seller does not know the parameters of the products' linear demand model, and can dynamically adjust product prices to learn the demand model based on sales observations.
Sobolev Norm Learning Rates for Conditional Mean Embeddings
We develop novel learning rates for conditional mean embeddings by applying the theory of interpolation for reproducing kernel Hilbert spaces (RKHS).
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