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arXiv Machine Learning By Yasir Zubayr Barlas, Sabina J. Sloman, Samuel Kaski

Robust Experimental Design via Generalised Bayesian Inference

Read the original on arXiv Machine Learning →

The paper introduces Generalised Bayesian Optimal Experimental Design (GBOED), extending generalised Bayesian inference to experimental design. It replaces the likelihood in Bayesian updates with a loss function, deriving a new acquisition function called Gibbs expected information gain. Empirical results show that GBOED improves robustness against outliers and mis-specified outcome noise distributions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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