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stat.ML2026

Stationary MMD Points

Zonghao Chen, Toni Karvonen, Heishiro Kanagawa +2

Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical integration. Several authors have proposed to se…

stat.ML2025

BayesSum: Bayesian Quadrature in Discrete Spaces

Sophia Seulkee Kang, François-Xavier Briol, Toni Karvonen +1

This paper addresses the challenging computational problem of estimating intractable expectations over discrete domains. Existing approaches, including Monte Carlo and Russian Roul…

stat.ML2025

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…

stat.ML2025

(De)-regularized Maximum Mean Discrepancy Gradient Flow

Zonghao Chen, Aratrika Mustafi, Pierre Glaser +3

We introduce a (de)-regularization of the Maximum Mean Discrepancy (DrMMD) and its Wasserstein gradient flow. Existing gradient flows that transport samples from source distributio…

stat.ML2025

Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization

Zonghao Chen, Atsushi Nitanda, Arthur Gretton +1

We establish the first global convergence result of neural networks for two stage least squares (2SLS) approach in nonparametric instrumental variable regression (NPIV). This is ac…

stat.ML2025

Nested Expectations with Kernel Quadrature

Zonghao Chen, Masha Naslidnyk, François-Xavier Briol

This paper considers the challenging computational task of estimating nested expectations. Existing algorithms, such as nested Monte Carlo or multilevel Monte Carlo, are known to b…