Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

• URL: http://arxiv.org/abs/2103.02438v1
• Date: Wed, 3 Mar 2021 14:43:48 GMT
• Title: Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
• Authors: Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth
• Abstract summary: We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of performing sequential adaptive experiments. We demonstrate that DAD successfully amortizes the process of experimental design, outperforming alternative strategies on a number of problems.
• Score: 11.414086057582324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED). Traditional sequential BOED approaches require substantial computational time at each stage of the experiment. This makes them unsuitable for most real-world applications, where decisions must typically be made quickly. DAD addresses this restriction by learning an amortized design network upfront and then using this to rapidly run (multiple) adaptive experiments at deployment time. This network takes as input the data from previous steps, and outputs the next design using a single forward pass; these design decisions can be made in milliseconds during the live experiment. To train the network, we introduce contrastive information bounds that are suitable objectives for the sequential setting, and propose a customized network architecture that exploits key symmetries. We demonstrate that DAD successfully amortizes the process of experimental design, outperforming alternative strategies on a number of problems.

Related papers

• Amortized Bayesian Experimental Design for Decision-Making [22.250312394159945]
  We present an amortized decision-aware BED framework that prioritizes maximizing downstream decision utility. We introduce a novel architecture, the Transformer Neural Decision Process (TNDP), capable of instantly proposing the next experimental design. We demonstrate the performance of our method across several tasks, showing that it can deliver informative designs and facilitate accurate decision-making.
  arXiv  Detail & Related papers  (2024-11-04T13:06:46Z)
• Adaptive Experimentation When You Can't Experiment [55.86593195947978]
  This paper introduces the emphconfounded pure exploration transductive linear bandit (textttCPET-LB) problem. Online services can employ a properly randomized encouragement that incentivizes users toward a specific treatment.
  arXiv  Detail & Related papers  (2024-06-15T20:54:48Z)
• Task-specific experimental design for treatment effect estimation [59.879567967089145]
  Large randomised trials (RCTs) are the standard for causal inference. Recent work has proposed more sample-efficient alternatives to RCTs, but these are not adaptable to the downstream application for which the causal effect is sought. We develop a task-specific approach to experimental design and derive sampling strategies customised to particular downstream applications.
  arXiv  Detail & Related papers  (2023-06-08T18:10:37Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design [31.247108087199095]
  CO-BED is a model-agnostic framework for designing contextual experiments using information-theoretic principles. As a result, CO-BED provides a general and automated solution to a wide range of contextual optimization problems.
  arXiv  Detail & Related papers  (2023-02-27T18:14:13Z)
• Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models [25.00242490764664]
  Reinforcement Learning for Deep Adaptive Design (RL-DAD) is a method for simulation-based optimal experimental design for non-differentiable implicit models. RL-DAD maps prior histories to experiment designs offline and can be quickly deployed during online execution.
  arXiv  Detail & Related papers  (2022-03-08T18:47:01Z)
• Simulation Paths for Quantum Circuit Simulation with Decision Diagrams [72.03286471602073]
  We study the importance of the path that is chosen when simulating quantum circuits using decision diagrams. We propose an open-source framework that allows to investigate dedicated simulation paths.
  arXiv  Detail & Related papers  (2022-03-01T19:00:11Z)
• Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods [24.50829695870901]
  implicit Deep Adaptive Design (iDAD) is a new method for performing adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian optimal experimental design (BOED) by learning a design policy network upfront.
  arXiv  Detail & Related papers  (2021-11-03T16:24:05Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions [80.78077900288868]
  We decompose the design methodology of early-exit networks to its key components and survey the recent advances in each one of them. We position early-exiting against other efficient inference solutions and provide our insights on the current challenges and most promising future directions for research in the field.
  arXiv  Detail & Related papers  (2021-06-09T12:33:02Z)
• Optimal Experimental Design for Staggered Rollouts [11.187415608299075]
  We study the design and analysis of experiments conducted on a set of units over multiple time periods where the starting time of the treatment may vary by unit. We propose a new algorithm, the Precision-Guided Adaptive Experiment (PGAE) algorithm, that addresses the challenges at both the design stage and at the stage of estimating treatment effects.
  arXiv  Detail & Related papers  (2019-11-09T19:46:29Z)

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.