1 citations · 2 across the 27 of their papers we have counts for
4 papers · 2 filters
Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
Dimitri Meunier, Zhu Li, Tim Christensen +1
We study the kernel instrumental variable (KIV) algorithm, a kernel-based two-stage least-squares method for nonparametric instrumental variable regression. We provide a convergenc…
Interventional Processes for Causal Uncertainty Quantification
Hugh Dance, Peter Orbanz, Arthur Gretton
Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar es…
Conditional Bayesian Quadrature
Zonghao Chen, Masha Naslidnyk, Arthur Gretton +1
We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating integrands is costly. Through the framework o…
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
Dimitri Meunier, Zikai Shen, Mattes Mollenhauer +2
We study theoretical properties of a broad class of regularized algorithms with vector-valued output. These spectral algorithms include kernel ridge regression, kernel principal co…