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Found 5 papers, 1 papers with code
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
We present the first optimal rates for infinite-dimensional vector-valued ridge regression on a continuous scale of norms that interpolate between $L_2$ and the hypothesis space, which we consider as a vector-valued reproducing kernel Hilbert space.
Nonlinear Meta-Learning Can Guarantee Faster Rates
Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target task.
Optimal Rates for Regularized Conditional Mean Embedding Learning
We address the misspecified setting, where the target CME is in the space of Hilbert-Schmidt operators acting from an input interpolation space between $\mathcal{H}_X$ and $L_2$, to $\mathcal{H}_Y$.
Distribution Regression with Sliced Wasserstein Kernels
We study the theoretical properties of a kernel ridge regression estimator based on such representation, for which we prove universal consistency and excess risk bounds.
Meta-strategy for Learning Tuning Parameters with Guarantees
We consider an online meta-learning scenario, and we propose a meta-strategy to learn these parameters from past tasks.
