← Back to all news
arXiv Machine Learning By Chuntao Chen, Tapio Helin, Nuutti Hyv\"onen, Yuya Suzuki

Approximation of differential entropy in Bayesian optimal experimental design

Read the original on arXiv Machine Learning →

The paper presents a method for approximating differential entropy in Bayesian optimal experimental design, focusing on cases where the likelihood entropy is design‑independent or explicitly computable, such as additive noise models. By reducing the expected information gain to a single integral, the authors propose using Monte Carlo or quasi‑Monte Carlo surrogates for the evidence density while evaluating differential entropy with standard techniques, thereby avoiding extra likelihood evaluations. Theoretical analysis shows convergence rates comparable to or better than existing methods, and numerical experiments support these findings.

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.

Related stories

arXiv Machine Learning
Oct 19

Robust Experimental Design via Generalised Bayesian Inference

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.

By Yasir Zubayr Barlas, Sabina J. Sloman, Samuel Kaski
More like this →
arXiv Machine Learning
Oct 9

Exploiting Gradients in Bayesian Inference of Expensive Simulators

The paper explores how to use gradients of simulator outputs with respect to input parameters to improve Bayesian inference for expensive simulators. By incorporating gradient information into Gaussian process surrogate models, the authors show that Bayesian optimization can converge faster under a limited simulation budget. They find that reverse‑mode differentiation yields efficiency gains, while forward‑mode does not offset its computational cost, especially when the number of parameters exceeds the output dimensionality.

By \v{S}imon Sold\'at, V\'aclav \v{S}m\'idl
efficiency
More like this →