Gaussian Process Batch Selection with Hallucinated Observations
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Solution Overview
Problem
Existing methods for selecting input data for parallel evaluation struggle to balance exploration and exploitation effectively, especially in scenarios with costly experiments and delayed feedback, leading to suboptimal results and increased cumulative regret.
Innovation Solution
A system that models the function as a Gaussian process, uses upper confidence bounds to select input data for parallel evaluation, and hallucinates intermediate observations to guide decision-making, allowing for efficient batch selection without relying on previous evaluation outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequential experimentation is used to balance exploration and exploitation, then decision accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The system pre-selects a batch of input data points before parallel evaluation based on upper confidence bound calculations. This preliminary selection ensures that even though results arrive simultaneously without intermediate feedback, the chosen points are optimally balanced for both exploration and exploitation, achieving sequential-like decision accuracy in parallel execution.
Solution Approach 2:
The upper confidence bound calculation serves as an intermediary mechanism that translates uncertain function evaluations into actionable batch selections. By computing UCB values that incorporate both exploration potential and exploitation promise, the system mediates between the competing goals without requiring intermediate feedback during parallel execution.
2Productivity
If parallel batch evaluation is implemented to increase processing speed, then productivity is improved, but decision quality deteriorates due to lack of intermediate feedback
Solution Approach 1:
The system performs preliminary batch selection using upper confidence bound calculations before parallel evaluation begins. This pre-computation of optimal batch members ensures that decision quality is maintained despite the absence of intermediate feedback during parallel execution, as the batch is carefully constructed to balance exploration and exploitation objectives.
Solution Approach 2:
The system creates a virtual model of the evaluation process through upper confidence bound calculations, allowing batch selection to be made based on predicted outcomes rather than actual intermediate results. This copying of the decision-making process enables parallel execution without sacrificing decision quality.
3Loss of time
If batch size is increased to improve parallelism and reduce time, then time efficiency is improved, but cumulative regret increases
Solution Approach 1:
The system dynamically adjusts the batch size parameter based on the upper confidence bound calculations and exploration-exploitation balance requirements. By changing the batch size parameter adaptively rather than using a fixed large batch, the system maintains time efficiency while controlling cumulative regret through mathematically grounded selection criteria.
Solution Approach 2:
The system selects a partial batch of input data points that are most promising according to upper confidence bound calculations, rather than evaluating all possible points in parallel. This partial action approach achieves good parallelism and time efficiency while maintaining decision quality and controlling cumulative regret by focusing computational resources on the most valuable subset.
Data Source
AI summary
A method and system for selecting a batch of input data from available input data for parallel evaluation by a function is disclosed. The function is modeled as drawn from a Gaussian process. Observations are used to determine a mean and a variance of the modeled function. An upper confidence bound is determined from the determined mean and variance. A decision rule is applied to select input data from the available input data to add to the batch of input data. The selection of the input data is based on a domain-specific time varying parameter. Intermediate observations are hallucinated within the batch. The hallucinated observations are used with the decision rule to select subsequent input data from the available input data for the batch of input data. The input data of the batch is evaluated in parallel with the function. The resulting determined data outputs are stored.