9 citations · 15 across the 14 of their papers we have counts for
13 papers · 1 filter
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
Jakub Wornbard, Zikai Shen, Dimitri Meunier +1
We develop semiparametrically efficient inference for kernel measures of noise heterogeneity in additive noise models. In many applications, the regression function is estimated us…
Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
Zikai Shen, Nathan Kallus, Dimitri Meunier +3
We consider debiased inference on finite-dimensional functionals of infinite-dimensional least-squares solutions to inverse problems as a way to avoid having to assume exact soluti…
Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
Dimitri Meunier, Jakub Wornbard, Vladimir R. Kostic +5
We address the problem of causal effect estimation in the presence of hidden confounders using nonparametric instrumental variable (IV) regression. An established approach is to us…
Nonparametric Instrumental Variable Regression with Observed Covariates
Zikai Shen, Zonghao Chen, Dimitri Meunier +3
We study the problem of nonparametric instrumental variable regression with observed covariates, which we refer to as NPIV-O. Compared with standard nonparametric instrumental vari…
Demystifying Spectral Feature Learning for Instrumental Variable Regression
Dimitri Meunier, Antoine Moulin, Jakub Wornbard +2
We address the problem of causal effect estimation in the presence of hidden confounders, using nonparametric instrumental variable (IV) regression. A leading strategy employs spec…
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
Juno Kim, Dimitri Meunier, Arthur Gretton +2
We provide a convergence analysis of deep feature instrumental variable (DFIV) regression (Xu et al., 2021), a nonparametric approach to IV regression using data-adaptive features…
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…
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…
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
Zhu Li, Dimitri Meunier, Mattes Mollenhauer +1
We present the first optimal rates for infinite-dimensional vector-valued ridge regression on a continuous scale of norms that interpolate between and the hypothesis space, w…
Nonlinear Meta-Learning Can Guarantee Faster Rates
Dimitri Meunier, Zhu Li, Arthur Gretton +1
Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target ta…
Optimal Rates for Regularized Conditional Mean Embedding Learning
Zhu Li, Dimitri Meunier, Mattes Mollenhauer +1
We address the consistency of a kernel ridge regression estimate of the conditional mean embedding (CME), which is an embedding of the conditional distribution of given int…
Distribution Regression with Sliced Wasserstein Kernels
Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto
The problem of learning functions over spaces of probabilities - or distribution regression - is gaining significant interest in the machine learning community. A key challenge beh…
Meta-strategy for Learning Tuning Parameters with Guarantees
Dimitri Meunier, Pierre Alquier
Online learning methods, like the online gradient algorithm (OGA) and exponentially weighted aggregation (EWA), often depend on tuning parameters that are difficult to set in pract…