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Found 6 papers, 3 papers with code
Distributionally Robust Beamforming and Estimation of Wireless Signals
This paper investigates signal estimation in wireless transmission from the perspective of statistical machine learning, where the transmitted signals may be from an integrated sensing and communication system; that is, 1) signals may be not only discrete constellation points but also arbitrary complex values; 2) signals may be spatially correlated.
Uncertainty-Aware Bayes' Rule and Its Applications
Philosophically, the key is to balance the relative importance of prior and data distributions when calculating posterior distributions: if prior (resp.
Robust Waveform Design for Integrated Sensing and Communication
Therefore, we formulate robust waveform design problems by studying the worst-case channels and prove that the robustly-estimated performance is guaranteed to be attainable in real-world operation.
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity
Trustworthy machine learning aims at combating distributional uncertainties in training data distributions compared to population distributions.
Distributional Robustness Bounds Generalization Errors
Third, we show that generalization errors of machine learning models can be characterized using the distributional uncertainty of the nominal distribution and the robustness measures of these machine learning models, which is a new perspective to bound generalization errors, and therefore, explain the reason why distributionally robust machine learning models, Bayesian models, and regularization models tend to have smaller generalization errors in a unified manner.
Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects
This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system.
