1 code implementation • 18 May 2023 • Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
We provide a variant of our estimator for conditional moment restrictions and show that it is asymptotically first-order optimal for such problems.
no code implementations • 26 Oct 2021 • Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
We provide a functional view of distributional robustness motivated by robust statistics and functional analysis.
no code implementations • 24 Jun 2021 • Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Random features is a powerful universal function approximator that inherits the theoretical rigor of kernel methods and can scale up to modern learning tasks.
1 code implementation • 16 Feb 2021 • Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf
We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization.