Design Amortization for Bayesian Optimal Experimental Design
- URL: http://arxiv.org/abs/2210.03283v1
- Date: Fri, 7 Oct 2022 02:12:34 GMT
- Title: Design Amortization for Bayesian Optimal Experimental Design
- Authors: Noble Kennamer, Steven Walton, Alexander Ihler
- Abstract summary: We build off of successful variational approaches, which optimize a parameterized variational model with respect to bounds on the expected information gain (EIG)
We present a novel neural architecture that allows experimenters to optimize a single variational model that can estimate the EIG for potentially infinitely many designs.
- Score: 70.13948372218849
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Bayesian optimal experimental design is a sub-field of statistics focused on
developing methods to make efficient use of experimental resources. Any
potential design is evaluated in terms of a utility function, such as the
(theoretically well-justified) expected information gain (EIG); unfortunately
however, under most circumstances the EIG is intractable to evaluate. In this
work we build off of successful variational approaches, which optimize a
parameterized variational model with respect to bounds on the EIG. Past work
focused on learning a new variational model from scratch for each new design
considered. Here we present a novel neural architecture that allows
experimenters to optimize a single variational model that can estimate the EIG
for potentially infinitely many designs. To further improve computational
efficiency, we also propose to train the variational model on a significantly
cheaper-to-evaluate lower bound, and show empirically that the resulting model
provides an excellent guide for more accurate, but expensive to evaluate bounds
on the EIG. We demonstrate the effectiveness of our technique on generalized
linear models, a class of statistical models that is widely used in the
analysis of controlled experiments. Experiments show that our method is able to
greatly improve accuracy over existing approximation strategies, and achieve
these results with far better sample efficiency.
Related papers
- A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Design [0.0]
We introduce LF-GO-OED (likelihood-free goal-oriented optimal experimental design), a computational method for conducting GO-OED with nonlinear observation and prediction models.
It is specifically designed to accommodate implicit models, where the likelihood is intractable.
The method is validated on benchmark problems with existing methods, and demonstrated on scientific applications of epidemiology and neural science.
arXiv Detail & Related papers (2024-08-18T19:45:49Z) - Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization.
We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons.
Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
arXiv Detail & Related papers (2024-03-20T00:41:12Z) - Enhanced Bayesian Optimization via Preferential Modeling of Abstract
Properties [49.351577714596544]
We propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into surrogate modeling.
We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments.
arXiv Detail & Related papers (2024-02-27T09:23:13Z) - Variational Sequential Optimal Experimental Design using Reinforcement
Learning [0.0]
We introduce variational sequential Optimal Experimental Design (vsOED), a new method for optimally designing a finite sequence of experiments under a Bayesian framework and with information-gain utilities.
Our vsOED results indicate substantially improved sample efficiency and reduced number of forward model simulations compared to previous sequential design algorithms.
arXiv Detail & Related papers (2023-06-17T21:47:19Z) - Statistically Efficient Bayesian Sequential Experiment Design via
Reinforcement Learning with Cross-Entropy Estimators [15.461927416747582]
Reinforcement learning can learn amortised design policies for designing sequences of experiments.
We propose the use of an alternative estimator based on the cross-entropy of the joint model distribution and a flexible proposal distribution.
Our method overcomes the exponential-sample complexity of previous approaches and provide more accurate estimates of high EIG values.
arXiv Detail & Related papers (2023-05-29T00:35:52Z) - Online simulator-based experimental design for cognitive model selection [74.76661199843284]
We propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods.
In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to 2 orders of magnitude less time than existing LFI alternatives.
arXiv Detail & Related papers (2023-03-03T21:41:01Z) - When to Update Your Model: Constrained Model-based Reinforcement
Learning [50.74369835934703]
We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL)
Our follow-up derived bounds reveal the relationship between model shifts and performance improvement.
A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
arXiv Detail & Related papers (2022-10-15T17:57:43Z) - Bayesian Optimal Experimental Design for Simulator Models of Cognition [14.059933880568908]
We combine recent advances in BOED and approximate inference for intractable models to find optimal experimental designs.
Our simulation experiments on multi-armed bandit tasks show that our method results in improved model discrimination and parameter estimation.
arXiv Detail & Related papers (2021-10-29T09:04:01Z) - Optimal Bayesian experimental design for subsurface flow problems [77.34726150561087]
We propose a novel approach for development of chaos expansion (PCE) surrogate model for the design utility function.
This novel technique enables the derivation of a reasonable quality response surface for the targeted objective function with a computational budget comparable to several single-point evaluations.
arXiv Detail & Related papers (2020-08-10T09:42:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.