Optimal Experimental Design Methods

Neueste Paper

• Resolution-Aware Experimental Design under Partial Identifiability

  Sofianos Panagiotis Fotias · 4. September 2026

  Experimental design is commonly framed as choosing the experiment expected to provide the most information. Under partial identifiability however, persistent nuisance uncertainty can make the same observation carry different structural meanings. We introduce Resolution-Aware Experimental Design (RAE…

• Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design

  Manisha Dubey, Rimvydas Rubavicius, N. Siddharth, Subramanian Ramamoorthy · 3. August 2026

  Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior. Bayesian inverse planning provides a principled framework for such inference, but its success depends critically on the experimental environment. Existing approaches typically treat environments…

• Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation

  Qining Zhang, Tanner Fiez, Yi Liu, Wenyang Liu · 13. Juli 2026

  A/B tests in online experiments face statistical power challenges when testing multiple candidates simultaneously, while adaptive experimental designs (AED) alone fall short in inferring experiment statistics such as the average treatment effect, especially with many metrics (e.g., revenue, safety) …

• Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives

  Tom Rossa, Angus Phillips, Tom Rainforth · 23. Juni 2026

  Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfall of this approach is that it leads to doubly intractable objectives that are difficult to optimise, while customising them to particular downstream …

• Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty

  Jinwoo Go, Xiaoning Qian, Byung-Jun Yoon · 26. Mai 2026

  Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical settings, reducing parameter uncertainty does not necessarily improve downstream decisions, as only specific parameter directions relevant to the objecti…

• Maximin Robust Bayesian Experimental Design

  Hany Abdulsamad, Sahel Iqbal, Christian A. Naesseth, Takuo Matsubara, Adrien Corenflos · 17. März 2026

  We address the brittleness of Bayesian experimental design under model misspecification by formulating the problem as a max--min game between the experimenter and an adversarial nature subject to information-theoretic constraints. We demonstrate that this approach yields a robust objective governed …

• Bayesian Experimental Design for Model Discrepancy Calibration: A Rivalry between Kullback--Leibler Divergence and Wasserstein Distance

  Huchen Yang, Xinghao Dong, Jin-Long Wu · 26. Januar 2026

  Designing experiments that systematically gather data from complex physical systems is central to accelerating scientific discovery. While Bayesian experimental design (BED) provides a principled, information-based framework that integrates experimental planning with probabilistic inference, the sel…

• Beyond Basic A/B testing: Improving Statistical Efficiency for Business Growth

  Changshuai Wei, Phuc Nguyen, Benjamin Zelditch, Joyce Chen · 12. Dezember 2025

  The standard A/B testing approaches are mostly based on t-test in large scale industry applications. These standard approaches however suffers from low statistical power in business settings, due to nature of small sample-size or non-Gaussian distribution or return-on-investment (ROI) consideration.…

• Robust Experimental Design via Generalised Bayesian Inference

  Yasir Zubayr Barlas, Sabina J. Sloman, Samuel Kaski · 12. November 2025

  Bayesian optimal experimental design is a principled framework for conducting experiments that leverages Bayesian inference to quantify how much information one can expect to gain from selecting a certain design. However, accurate Bayesian inference relies on the assumption that one's statistical mo…

• Efficient Adaptive Experimentation with Noncompliance

  Miruna Oprescu, Brian M Cho, Nathan Kallus · 30. Oktober 2025

  We study the problem of estimating the average treatment effect (ATE) in adaptive experiments where treatment can only be encouraged -- rather than directly assigned -- via a binary instrumental variable. Building on semiparametric efficiency theory, we derive the efficiency bound for ATE estimation…

• Efficient Randomized Experiments Using Foundation Models

  Piersilvio De Bartolomeis, Javier Abad, Guanbo Wang, Konstantin Donhauser, Raymond M. Duch, Fanny Yang, Issa J. Dahabreh · 28. Oktober 2025

  Randomized experiments are the preferred approach for evaluating the effects of interventions, but they are costly and often yield estimates with substantial uncertainty. On the other hand, in silico experiments leveraging foundation models offer a cost-effective alternative that can potentially att…