1 citations · 1 across the 4 of their papers we have counts for
5 papers
Estimation Beyond Data Reweighting: Kernel Method of Moments
Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf +1
Moment restrictions and their conditional counterparts emerge in many areas of machine learning and statistics ranging from causal inference to reinforcement learning. Estimators f…
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee
Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf +1
This paper is motivated by addressing open questions in distributionally robust chance-constrained programs (DRCCP) using the popular Wasserstein ambiguity sets. Specifically, the…
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control
Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
We provide a functional view of distributional robustness motivated by robust statistics and functional analysis. This results in two practical computational approaches for approxi…
Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics
Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf +1
Random features is a powerful universal function approximator that inherits the theoretical rigor of kernel methods and can scale up to modern learning tasks. This paper views unce…
Reliable Real Time Ball Tracking for Robot Table Tennis
Sebastian Gomez-Gonzalez, Yassine Nemmour, Bernhard Schölkopf +1
Robot table tennis systems require a vision system that can track the ball position with low latency and high sampling rate. Altering the ball to simplify the tracking using for in…