HLinUCB Engine for Biased Historical Data Initialization
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Solution Overview
Problem
Machine learning computer models require extensive manual effort and resources for training, and existing methods for multi-armed bandit problems face challenges with bias and data corruption in historical data, leading to suboptimal performance and increased regret in reward maximization.
Innovation Solution
The implementation of a balanced Historical Linear Upper Confidence Bound (HLinUCB) engine that initializes machine learning models using propensity scores and trimmed optimization to mitigate bias and corruption in historical data, improving the robustness and efficiency of contextual multi-armed bandit algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning training methods are used, then model accuracy can be achieved, but the process is very time consuming and resource consuming intensive
Solution Approach 1:
The patent applies preliminary action by performing offline initialization of the multi-armed bandit algorithm using historical data before online operation. The offline phase pre-computes initial parameters and propensity scores from historical observations, so that when the online phase begins, the algorithm starts with informed priors rather than random initialization. This preliminary preparation reduces the exploration time needed during online operation while maintaining accuracy.
Solution Approach 2:
The patent changes parameters by transitioning from traditional supervised learning parameters (weights, biases) to multi-armed bandit parameters (propensity scores, upper confidence bounds). The algorithm uses parameter initialization from historical data and dynamically adjusts exploration-exploitation balance through parameter updates based on observed rewards, enabling faster convergence with fewer training iterations.
2Speed
If historical data is used to initialize the multi-armed bandit algorithm, then convergence speed improves, but bias and data corruption in historical data lead to suboptimal performance
Solution Approach 1:
The patent implements feedback by continuously monitoring the performance of the initialized algorithm and using observed rewards to update the upper confidence bounds and parameter estimates. The system compares predicted outcomes with actual outcomes, and this feedback loop allows the algorithm to correct any biases introduced by historical data over time, ensuring that initial advantages don't become permanent disadvantages.
Solution Approach 2:
The patent extracts only the useful information from historical data while discarding potentially corrupted portions. By using historical data solely for initialization of propensity scores and then relying on online observations for ongoing decisions, the system separates the beneficial signal (patterns that can be learned from history) from the noisy or corrupted portions, reducing the impact of data quality issues.
3Measurement precision
If exploration strategies are emphasized to discover optimal actions, then long-term reward maximization improves, but short-term performance and resource utilization decrease
Solution Approach 1:
The patent applies dynamics by making the exploration-exploitation balance adaptive rather than static. The upper confidence bound parameter dynamically adjusts based on the number of times each action has been taken - actions with fewer observations have higher bounds (encouraging exploration), while well-explored actions have lower bounds (encouraging exploitation). This dynamic balancing allows the system to automatically adjust between exploration and exploitation based on current knowledge state.
Solution Approach 2:
The patent changes the exploration parameter (upper confidence bound) as a function of observation count. The parameter δ/a(t) decreases as t increases, meaning exploration intensity automatically diminishes as more data is collected. This parameter transformation allows the system to start with high exploration when uncertain and gradually shift toward exploitation as confidence increases, optimizing both short-term and long-term performance.
Data Source
AI summary
Mechanisms are provided for training a machine learning computer model. The mechanisms execute a first initialization of machine learning training logic based on a determination of propensity scores for each output, of a plurality of predetermined outputs, of a machine learning computer model, the propensity scores being determined from historical data. The mechanisms execute a second initialization of the machine learning training logic by performing a trimmed optimization of the machine learning training logic, based on the historical data, to estimate initial parameters of the machine learning computer model. The resulting initialized machine learning training logic is executed on the machine learning computer model to train the machine learning computer model which is then deployed.