Bayesian Experimental Design

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Bayesian experimental design studies how to choose experiments or measurements sequentially when observations are costly and uncertainty matters. Common approaches select designs by expected information gain or posterior uncertainty, often using Gaussian processes, Monte Carlo estimates, or amortized neural policies; Bayesian optimization and active learning extend these ideas to expensive black-box problems. Applications range from scientific parameter estimation and sensor placement to clinical questioning, reliability testing, and adaptive data collection. A central tension is that the most informative experiment may not improve a downstream decision, while model error, constraints, and computational cost can make standard information-gain criteria unreliable or impractical.

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63 papers10 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Evidence Acquisition and Stopping via Lattice Distillation
Jun 8, 2026Yixuan Zhang, Yang Song, Jiayi Zhang +4Epidemic Forecasting Selective Prediction
Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark · Section of Health Data Science & AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark · Rutgers University, New Brunswick, NJ, USA +1
ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation
Jun 1, 2026David Rundel, Fabian Fumagalli, Maximilian Muschalik +2Shapley Value Attribution Adaptive Sampling
Department of Statistics, LMU Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany · Institute for Informatics, LMU Munich, Munich, Germany +2
CA-BED: Conversation-Aware Bayesian Experimental Design
May 31, 2026Daniel Arnould, Rashad Aziz, Zixuan Kang +5Active Information Gathering Bayesian Experimental Design
When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery
May 26, 2026Neel Tushar Shah, Manglam KartikModel Misspecification Autonomous Scientific Discovery
Indian Institute of Technology Bombay, Mumbai, India.
Constrained Bayesian Experimental Design via Online Planning
May 26, 2026Yujia Guo, Daolang Huang, Xinyu Zhang +3Bayesian Experimental Design Sequential Experimental Design
ELLIS Institute Finland · Department of Computer Science, Aalto University, Finland · Department of Computer Science, University of Manchester, UK
Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty
May 25, 2026Jinwoo Go, Xiaoning Qian, Byung-Jun YoonBayesian Experimental Design Decision-Making under Uncertainty
Computing & Data Sciences, Brookhaven National Laboratory, Upton, NY · Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX · Department of Computer Science & Engineering, Texas A&M University, College Station, TX
Exploration and Experimentation with a Misspecified Simulator
May 20, 2026Harsh Parikh, Gabriel Levin-Konigsberg, Sohrab Andaz +3Sim-to-Real Transfer RL Exploration
Duke University, Durham, USA · Yale University, New Haven, USA
Elicitation-Augmented Bayesian Optimization
May 12, 2026Alvar Haltia, Ville Hyvönen, Samuel KaskiBayesian Optimization Human-in-the-Loop Evaluation
Aalto University · University of Helsinki · Aalto University, University of Manchester
Many Needles in a Haystack: Active Hit Discovery for Perturbation Experiments
May 11, 2026Andrea Rubbi, Arpit Merchant, Samuel Ogden +4Bayesian Optimization Bayesian Experimental Design
Department of Computer Science and Technology, University of Cambridge, Cambridge, UK · Cambridge Center for AI in Medicine, University of Cambridge, Cambridge, UK · MediaTek Research, Cambridge, UK +1
DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments
May 7, 2026Kateryna Husar, Alexander VolfovskyThompson Sampling Causal Effect Estimation
Department of Statistical Science Duke University Durham, NC 27705
The Perceptual Bandwidth Bottleneck in Vision-Language Models: Active Visual Reasoning via Sequential Experimental Design
May 2, 2026Anjie Liu, Ziqin Gong, Yan Song +6VLM Reasoning Visually Grounded Reasoning
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · University College London, London, United Kingdom · ShanghaiTech University, Shanghai, China +2
Adaptive Querying with AI Persona Priors
May 1, 2026Kaizheng Wang, Yuhang Wu, Assaf ZeeviLatent Variable Models Bayesian Experimental Design
Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University. · Decision, Risk, and Operations Division, Columbia Business School.
Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
Apr 26, 2026William RatcliffActive Learning Materials Science
NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection
Apr 24, 2026Sijie Li, Shanda Li, Haowei Lin +3Active Experimental Design Scaling Laws
Peking University · Carnegie Mellon University · Datadog
Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions
Apr 23, 2026Di Wu, Ling Liang, Haizhao YangBayesian Experimental Design Integral Probability Metrics
Department of Mathematics The University of Tennessee, Knoxville TN 37916, USA · Department of Mathematics and Department of Computer Science University of Maryland, College Park MD 20742, USA
Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Apr 1, 2026Zhongwei Yu, Rasul Tutunov, Alexandre Max Maraval +13Bayesian Optimization Scientific Discovery
The Hong Kong University of Science and Technology (Guangzhou) · Huawei Noah’s Ark Lab · University of Science and Technology of China +5
Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation
Mar 27, 2026S. A. K. Leeney, T. Gessey-Jones, W. J. Handley +3Bayesian Experimental Design Simulation-Based Inference
Cavendish Astrophysics, University of Cambridge, Cambridge CB3 0HE, UK. · Kavli Institute for Cosmology, University of Cambridge, Cambridge CB3 0HA, UK. · PhysicsX, London, UK. +1
Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization
Mar 27, 2026Ajith Anil Meera, Wouter KouwFree Energy Calculation Bayesian Optimization
Faculty of Electrical Engineering, TU Eindhoven, The Netherlands
ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
Mar 22, 2026Foo Hui-Mean, Yuan-chin I ChangSequential Experimental Design Active Experimental Design
Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
Deep Adaptive Model-Based Design of Experiments
Mar 17, 2026Arno Strouwen, Sebastian Micluţa-CâmpeanuBayesian Experimental Design Nonlinear System Identification
Strouwen Statistics, Hasselt, Belgium · Biosystems Department, KU Leuven, Belgium · JuliaHub, Massachusetts, USA +1
Maximin Robust Bayesian Experimental Design
Mar 14, 2026Hany Abdulsamad, Sahel Iqbal, Christian A. Naesseth +2Bayesian Experimental Design Mutual Information
UvA–Bosch Delta Lab University of Amsterdam · Department of Statistics University of Oxford · School of Mathematics University of Edinburgh +1
Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
Mar 12, 2026Kaushik Bhattacharya, Lianghao Cao, Andrew StuartBayesian Experimental Design Model Calibration
California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA.
Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
Mar 12, 2026Louis SharrockBayesian Experimental Design Wasserstein Gradient Flows
Department of Statistical Science, University College London.
Observationally Informed Adaptive Causal Experimental Design
Mar 4, 2026Erdun Gao, Liang Zhang, Jake Fawkes +5Active Experimental Design Causal Inference
Australian Institute for Machine Learning, Adelaide University · Hong Kong University of Science and Technology (Guangzhou) · University College London +3
SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing
Mar 2, 2026Anjali Parashar, Yingke Li, Eric Yang Yu +4AI Safety Evaluation Benchmark Design
Laboratory for Information & Decision Systems (LIDS), MIT · Saab Inc.
Correcting Boundary Bias and Observation Independence in Bayesian Experimental Design
Feb 2, 2026Sanna Jarl, Jens Sjölund, Jonathan J. S. Scragg +1Active Learning Adaptive Sampling
Department of Material Science & Engineering Uppsala University RISE · Department of Information Technology Uppsala University · RISE
A Probabilistic Approach to Trajectory-Based Optimal Experimental Design
Jan 16, 2026Ahmed AttiaStochastic Optimization Trajectory Optimization
Mathematics and Computer Science Division, Argonne National Lab, USA
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
Dec 28, 2025Niels Bracher, Lars Kühmichel, Desi R. Ivanova +3Parameter Estimation Amortized Inference
1Rensselaer Polytechnic Institute, USA · 2TU Dortmund University, Germany · University of Oxford, UK.
Robust Experimental Design via Generalised Bayesian Inference
Nov 10, 2025Yasir Zubayr Barlas, Sabina J. Sloman, Samuel KaskiGeneralized Bayesian Inference Bayesian Experimental Design
The University of Manchester · Aalto University
Approximation of differential entropy in Bayesian optimal experimental design
Oct 1, 2025Chuntao Chen, Tapio Helin, Nuutti Hyvönen +1Bayesian Experimental Design Value of Information

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Evidence Acquisition and Stopping via Lattice Distillation

Yixuan Zhang, Yang Song, Jiayi Zhang, Jingqi Sun+3

Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark · Section of Health Data Science & AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark · Rutgers University, New Brunswick, NJ, USA+1

Epidemic Forecasting Selective Prediction Bayesian Inference Cost-Aware Inference Bayesian Experimental Design

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Sequential evidence acquisition requires deciding what to observe and when to stop from partial information. We introduce a complete-record view of policy supervision: each fully observed training instance defines a lattice of evidence subsets, with a stop/query decision at every node. For a modest number of evidence groups, this structure can be enumerated even when individual groups contain rich representations. Our method, Subset Lattice Distillation (SLD), scores subsets with a shared masked predictor, constructs multi-step decision targets by cost-sensitive backward planning, and distills them into an observed-history controller. Uniformly resampled masks train the student across the subset space, while combinatorial planning remains offline. Separating subset scores from acquisition prices allows the predictor to be reused when the policy is replanned for new costs. Our analysis identifies action-margin conditions under which privileged supervision preserves optimal online decisions. On retrospective influenza monitoring, SLD achieves higher phase F1 at lower acquisition cost than both the closest-cost adaptive baselines and the fixed full-evidence model. A geographic and seasonal holdout, price perturbations, and ablation studies provide further evaluation.