Combines multi-fidelity and asynchronous batch methods for faster experimental design.
problem Designing optimal experimental setups for battery performance.
method Algorithm combining multi-fidelity and asynchronous batch Bayesian Optimization.
result Algorithm outperforms single-fidelity batch and multi-fidelity sequential methods.
Expands Bayesian experiment design framework to account for model discrepancies.
problem Model misspecification in Bayesian optimal experiment design.
method Introduces Expected General Information Gain and Expected Discriminatory Information criteria.
result Demonstrates improved robustness and detection capabilities in experiment design.
DAD learns to design experiments quickly, outperforming traditional methods.
problem Real-time decision-making in sequential Bayesian experimental design.
method Amortized design network trained with contrastive information bounds.
result DAD outperforms alternative strategies on various problems.
Bayesian methods improve drug discovery experiment design.
problem Optimizing drug screening experiments in high-dimensional data.
method Bayesian inference and optimisation with upper confidence bound algorithms, Thompson sampling, and sparse tree search.
result Sparse tree search techniques outperform other methods in drug toxicity screening.
Design of experiments improves validation of biomolecular networks.
problem Efficiently validate non-machine learning designed biomolecular networks.
method Use Gaussian processes and Bayesian optimization to select experimental points.
result Developed a stopping criterion based on discrepancy metric and uncertainty.
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as p…
DABS uses a policy network to select experiments in high-dimensional design spaces.
problem Adaptive factorial screening in high-dimensional discrete design spaces.
method DABS learns a policy network offline to sequentially select experiments, incorporating sparsity and interactions via a spike-and-slab prior.
result DABS achieves superior accuracy and scalability over classical and Bayesian baselines under tight experimental budgets.
Bayesian design improves experimental optimization.
problem Computational challenges limit BED practical use.
method Recent advances in BED have reduced computational burdens.
result Effective BED design is now feasible.
Paper uses transfer learning and Bayesian optimization to reduce DNA sequence design experiments.
problem Designing many similar DNA sequences for specific applications is expensive and time-consuming.
method Combines transfer learning with Bayesian optimization to reduce experiment count.
result Total number of experiments can be significantly reduced by sharing information between tasks.
New method optimizes experiments under constraints.
problem Adapting BED to dynamic constraints in real-world tasks.
method Offline pre-training of an amortized policy and posterior network with online multi-step lookahead planning.
result Significantly more informative design sequences than existing methods.
CO-BED optimizes experiments using Bayesian methods and information theory.
problem Optimizing experiments in a context-dependent manner.
method Formalizes contextual optimization with Bayesian experimental design, employing information-theoretic principles and black-box variational methods.
result CO-BED provides a general solution for contextual optimization problems.
Step-DAD improves BED by periodically updating a design policy during experiments.
problem Improving flexibility and robustness in Bayesian experimental design.
method Semi-amortized, policy-based approach that updates a design policy during data collection.
result Consistently superior decision-making and robustness compared to current BED methods.
A new framework designs experiments for better decision-making.
problem Suboptimal experimental designs for downstream decision-making.
method Amortized decision-aware Bayesian Experimental Design (BED) with Transformer Neural Decision Process (TNDP).
result TNDP effectively designs experiments and facilitates accurate decision-making.
Enhances robustness in experimental design through Generalised Bayesian inference.
problem Poor inference and estimates of information gain when statistical model is incorrectly specified.
method Generalised Bayesian (Gibbs) inference framework applied to experimental design.
result GBOED enhances robustness to outliers and incorrect assumptions about noise distribution.
A new method designs batches for Bayesian optimization more efficiently.
problem Efficiently designing batches for Bayesian optimization to reduce total time.
method Minimal Terminal Variance (MTV) acquisition function, optimizing I-optimality criterion.
result MTV designs batches more efficiently than other methods, as shown by numerical experiments.
Implicit stochastic models, where the data-generation distribution is intractable but sampling is possible, are ubiquitous in the natural sciences. The models typically have free parameters that need to be inferred from data collected in scientific experiments. A fundamental question is how to design the experiments so…
Paper introduces IO-NPF for efficient Bayesian experimental design.
problem Efficient Bayesian experimental design in non-exchangeable settings.
method Inside-Out Nested Particle Filter (IO-NPF) for non-Markovian state-space models.
result IO-NPF achieves O(T2) computational complexity, improving efficiency. vsOED optimizes experiment design with reinforcement learning for Bayesian models.
problem Optimizing the sequence of experiments in Bayesian models for efficient data collection.
method Reinforcement learning with variational posterior approximations to optimize design policy.
result vsOED achieves superior sample efficiency compared to existing methods.
Bayesian sOED uses PG reinforcement learning for efficient experiment design.
problem Optimizing sequential experiments for nonlinear models with limited data.
method Formulated as POMDP, solved via PG methods with neural network parameterization.
result Demonstrated advantages over batch and greedy designs in contaminant source inversion.
We use deep reinforcement learning to optimize experimental designs efficiently.
problem Optimizing sequential experimental designs with limited exploration and black-box models.
method Reduced the optimal design problem to an MDP and solved it with deep reinforcement learning.
result Our approach achieves state-of-the-art performance on both continuous and discrete design spaces.
Novel framework optimizes experiments for implicit models using mutual information.
problem Optimizing experiments for intractable implicit models.
method Sequential Bayesian Experimental Design using Mutual Information.
result Framework efficiently estimates parameters with few iterations.
Efficiently designs experiments without integrating posterior distributions.
problem Computational inefficiency in Bayesian experimental design for PDE-based models.
method Likelihood-free approach using ANN to approximate conditional expectation.
result Significant reduction in observation model evaluations.
Bayesian model estimates treatment effects near cutoffs in regression discontinuity designs.
problem Estimating conditional average treatment effects in regression discontinuity designs.
method Develops a Bayesian additive regression tree (BART) model with linear leaf-level regressions.
result Adapts to different slopes on the running variable near the cutoff, providing interpretable inference.
Improves BED scalability for implicit models.
problem Designing experiments for implicit models with intractable data distributions.
method Hybrid gradient approach combining variational MI estimator, ES, and SGA.
result Significantly improves scalability of BED for implicit models.
We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED). Our approach utilizes variational lower bounds on the expected information gain (EIG) of an experiment that can be simultaneously optimized with respect to both the variational and design parameters. This allows the …
GoBOED optimizes experiments for specific decision-making objectives, improving downstream outcomes.
problem Reducing parameter uncertainty does not always improve decision-making in critical settings.
method Combines variational posterior surrogate and differentiable convex decision layer for gradient-based design optimization.
result GoBOED identifies designs that better align with specific decision objectives and reveals wider optimal design windows.
We present an adaptive approach to the construction of Gaussian process surrogates for Bayesian inference with expensive-to-evaluate forward models. Our method relies on the fully Bayesian approach to training Gaussian process models and utilizes the expected improvement idea from Bayesian global optimization. We adapt…
BioBO optimizes gene perturbation design using Bayesian optimization with biological priors.
problem Efficient design of genomic perturbation experiments in drug discovery.
method Integrates Bayesian optimization with multimodal gene embeddings and enrichment analysis.
result Improves labeling efficiency by 25-40% and identifies top-performing perturbations more effectively.
BoFire optimizes chemistry experiments using Bayesian Optimization.
problem Effective deployment of Bayesian Optimization in the chemical industry.
method Combines Bayesian Optimization with DoE strategies, providing a rich feature-set.
result BoFire enables seamless integration into RESTful APIs for real-world use.
PASOA optimizes Bayesian design by improving SMC samplers and EIG.
problem Sequential design optimization for accurate parameter inference.
method Sequential optimization using contrastive estimation, SMC samplers, and tempering.
result PASOA optimizes design and inference with improved consistency.
Bayesian optimization of antibodies learns from immune system evolution.
problem Efficiently optimizing antibody sequences in a large space of possibilities.
method Bayesian optimization guided by a generative model of evolving antibody sequences.
result CloneBO optimizes antibodies more efficiently than previous methods.
Novel neural architecture improves Bayesian experimental design efficiency.
problem Intractable evaluation of expected information gain (EIG) in Bayesian optimal experimental design.
method Develops a neural architecture that optimizes a single variational model for estimating EIG across many designs, using a lower bound for computational efficiency.
result Significantly improves accuracy in Bayesian experimental design with better sample efficiency.
iDAD uses neural networks to quickly adapt experiments without likelihoods.
problem Performing adaptive experiments in real-time with implicit models.
method iDAD learns a design policy network upfront to make quick design decisions.
result iDAD can make design decisions in milliseconds, unlike traditional BOED approaches.
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several …
Gradient-free framework for Bayesian experimental design in complex systems.
problem Optimal experimental design in systems where gradient information is unavailable.
method Combines EKI and ALDI for optimization and sampling, with approximations for scalable utility estimation.
result Demonstrates robust, accurate, and efficient experimental design in various complex systems.
Bayesian adaptive designs can be biased by active learning, especially with misspecified models.
problem Active learning bias in Bayesian adaptive experimental designs.
method Analysis of linear and preference learning models, empirical testing.
result Model misspecification and noise influence active learning bias in Bayesian designs.
Scientific experiments are usually expensive due to complex experimental preparation and processing. Experimental design is therefore involved with the task of finding the optimal experimental input that results in the desirable output by using as few experiments as possible. Experimenters can often acquire the knowled…
Paper proposes an unbiased optimization method for Bayesian experimental design.
problem Maximizing expected information gain in Bayesian experimental design.
method Randomized multilevel Monte Carlo (MLMC) method combined with stochastic gradient descent.
result An unbiased estimator for the gradient of expected information gain.
Bayesian approach for estimating heterogeneous treatment effects in RDD designs.
problem Heterogeneity in treatment effects in RDD designs can lead to misleading conclusions.
method Direct Bayesian Additive Regression Trees (BART) for modeling heterogeneous treatment effects.
result Flexibly captures complicated structures of heterogeneous treatment effects as a function of covariates.
This work uses RL to optimize batch experiments in SDOE.
problem Maximizing knowledge with limited resources and constraints.
method Reinforcement Learning for batch-sampling in Bayesian SDOE.
result The proposed algorithm optimizes experiment selection for multiple tasks.
New BO methods exploit parallel experiments, reducing search time and improving solution quality.
problem Limitation of Bayesian optimization in exploiting parallel experiments.
method Propose new parallel BO paradigms that exploit the structure of the system to partition the design space.
result Significantly reduce search time and increase probability of finding global solutions.
A scalable portfolio approach speeds up Bayesian optimization for noisy functions.
problem Efficiently selecting multiple designs in parallel for noisy, expensive black-box optimization.
method A portfolio approach that balances exploration and exploitation, using a scalable allocation strategy.
result Significant speed improvements over existing methods, with similar or better performance.
BOED improves SBI by optimizing experimental designs and inference functions.
problem Efficiently use experimental resources for better inference on complex models.
method Link mutual information bounds between SBI and BOED, optimizing both design and inference.
result BOED improves inference in real-world simulators in epidemiology and biology.
New method tackles constrained optimization in multi-fidelity Bayesian optimization.
problem Efficiently identifying feasible regions in constrained optimization problems.
method Proposes CMFBO method with novel acquisition functions.
result Demonstrates effectiveness on synthetic problems and real-world ICF and joint design problems.
Bayesian DOE accelerates experimental design with improved efficiency.
problem Enhancing experimental design efficiency and reliability.
method Bayesian framework, conditional density estimation, informative data selection.
result Significantly improved computational efficiency of experimental design.
LaMBO optimizes biological sequences using autoencoders and Bayesian optimization.
problem Bayesian optimization for drug design is limited by discrete, high-dimensional decision variables.
method Jointly trains denoising autoencoder with a Gaussian process head for gradient-based optimization in latent space.
result LaMBO outperforms genetic optimizers and requires no large pretraining corpus.
Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance. For implicit models, where the likelihood is intractable but sampling from the model is possible, this task is particularly difficult and therefore largely unexplored. This is mai…
A new method for experimental design focuses on predicting downstream quantities of interest.
problem Designs that maximize parameter learning may not maximize downstream quantity prediction.
method Likelihood-free goal-oriented optimal experimental design (LF-GO-OED) using ABC density ratio estimation.
result LF-GO-OED maximizes the expected information gain for downstream quantities.