Hybrid Bayesian-Frequentist Hyperparameter Determination
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Solution Overview
Problem
Existing machine learning methods face challenges in determining suitable hyperparameters, especially when training data is limited, leading to potential unsuitable hyperparameter selection.
Innovation Solution
A computer-implemented method using a probabilistic model, such as a Gaussian process or Bayesian neural network, that switches between Bayesian and frequentist approaches based on uncertainty conditions, with criteria like Kullback-Leibler divergence and entropy thresholds, to efficiently determine hyperparameters, reducing computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Bayesian approach is used to determine hyperparameters, then reliability of hyperparameter determination is improved, but computing resources consumption increases
Solution Approach 1:
The patent changes the approach parameter from purely Bayesian to a hybrid Bayesian-frequentist approach. By introducing a threshold parameter that switches between Bayesian and frequentist methods based on data availability, the system adapts computational intensity to match the reliability requirements of different data scenarios.
Solution Approach 2:
The patent makes the hyperparameter determination method dynamic by switching between Bayesian and frequentist approaches based on the amount of available training data. The system dynamically adjusts its computational strategy: using Bayesian methods when data is scarce for higher reliability, and frequentist methods when data is abundant for lower computational cost.
2Use of energy by moving object
If frequentist approach is used to determine hyperparameters, then computing resources consumption is reduced, but reliability of hyperparameter determination deteriorates when training data is limited
Solution Approach 1:
The patent introduces a feedback mechanism that monitors the amount of training data available and uses this feedback to select the appropriate method. The system continuously evaluates data availability and switches between Bayesian and frequentist approaches accordingly, ensuring reliable hyperparameter determination while optimizing computational resources.
Solution Approach 2:
The patent changes the method parameter based on data quantity conditions. By introducing a threshold parameter that triggers different methods based on training data availability, the system ensures reliable hyperparameter determination when data is limited while reducing computational cost when data is sufficient.
3Productivity
If early definition ofhyperparameter value is made, then productivity of machine learning process is improved, but reliability ofhyperparameter determination deteriorates under high uncertainty
Solution Approach 1:
The patent applies preliminary action by using Bayesian methods during the early stages of training when data is scarce, despite the higher computational cost. This preliminary Bayesian analysis establishes reliable initial hyperparameter estimates before switching to faster frequentist methods, ensuring reliability is established before productivity optimization.
Solution Approach 2:
The patent makes the hyperparameter determination process dynamic by switching methods based on data availability and uncertainty levels. The system dynamically adjusts between Bayesian (high reliability, lower productivity) and frequentist (low reliability, high productivity) methods to optimize the balance between reliability and productivity at different stages of training.
Data Source
AI summary
A device and computer-implemented method for machine learning. A probabilistic model is provided, in particular a model that includes a probability distribution, preferably a Gaussian process or a Bayesian neural network, the model being defined as a function of at least one hyperparameter, in particular of the Gaussian process or of the Bayesian neural network. In one iteration, an instruction for a first measurement is determined and output as a function of the model. For the at least one hyperparameter an a posteriori distribution over values for the at least one hyperparameter being determined as a function of the first measurement. In another iteration, an instruction for a second measurement is determined and output as a function of the model. At least one value of the at least one hyperparameter is determined as a function of the second measurement.