arXiv Machine Learning

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.

arXiv Machine Learning

Oct 12

Approximation of differential entropy in Bayesian optimal experimental design

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.

By Chuntao Chen, Tapio Helin, Nuutti Hyv\"onen, Yuya Suzuki

benchmarks

arXiv Machine Learning

Aug 14

Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design

arXiv:2502. 08004v2 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems.

By Vincent D. Zaballa, Elliot E. Hui

arXiv Machine Learning

Jul 10

Robust Bayesian Decision Making under Adversarial Uncertainty

arXiv:2607. 08590v1 Announce Type: new Abstract: Scientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream decision-making.

By Haripriya Harikumar, Sammie Katt, Yasir Zubayr Barlas, Samuel Kaski

safety

arXiv Statistics ML

Aug 25

Deep adaptive design with an evidential bias criterion

arXiv:2608.16466v2 Announce Type: replace-cross Abstract: Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an exp...

By David Chen, Michael Evans, Xinwei Li, Prateek Bansal, David J. Nott

safety

arXiv AI

Aug 3

Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design

arXiv:2607. 28894v1 Announce Type: new Abstract: Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior.

By Manisha Dubey, Rimvydas Rubavicius, N. Siddharth, Subramanian Ramamoorthy

benchmarks

arXiv Machine Learning

Jul 21

Deep Adaptive Bayesian Screening

arXiv:2607. 16927v1 Announce Type: cross Abstract: We introduce Deep Adaptive Bayesian Screening (DABS), a method for performing adaptive factorial screening in high-dimensional discrete design spaces.

By Jade Lejeune Herman, Arno Strouwen, Johan A. K. Suykens, Peter Goos

efficiency benchmarks

arXiv Machine Learning

Oct 16

Designing Experiments for External Decision Makers

The paper introduces usage‑aware experimental design, a framework that extends standard Bayesian experimental design to settings where external decision makers act on collected data. It trains adaptive design policies to minimise the experimenter’s expected loss under external rules, which may be non‑Bayesian or black‑box. The authors show that more informative data can be harmful under such rules, so usage risk cannot be expressed as expected uncertainty reduction, thereby generalising standard BED.

By Sahel Iqbal, Tom Rainforth

arXiv AI

Jul 7

JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference

arXiv:2512. 22999v2 Announce Type: replace-cross Abstract: We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain.

By Niels Bracher, Lars K\"uhmichel, Desi R. Ivanova, Xavier Intes, Paul-Christian B\"urkner, Stefan T. Radev

diffusion multimodal benchmarks

arXiv Machine Learning

Aug 4

Robust Bayesian Optimization via Tempered Posteriors

arXiv:2601. 07094v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function.

By Jiguang Li, Hengrui Luo

arXiv Machine Learning

Oct 8

Towards Regret Guarantees for One-Step Lookahead Bayesian Optimization

The paper investigates theoretical guarantees for a one‑step lookahead Bayesian optimization method called optimal‑point variance reduction (OVR). It derives a uniform Monte Carlo estimation error bound for the acquisition function and shows that a regularized version of OVR achieves a vanishing Bayesian expected simple regret bound. Numerical experiments validate the performance of both OVR and its regularized variant.

By Shion Takeno

arXiv Machine Learning

Jun 30

BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Search

arXiv:2406. 03616v5 Announce Type: replace-cross Abstract: Novelty search (NS) aims to uncover diverse system behaviors through simulation or experiment without requiring a pre-specified scalar objective.

By Wei-Ting Tang, Ankush Chakrabarty, Joel A. Paulson

benchmarks

arXiv Machine Learning

Oct 8

BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability

arXiv:2602.07144v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a popular technique for sample-efficient optimization of black-box functions. In many applications, the parameters be...

By Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy

safety

Related stories