Machine Learning Model Training via Importance Weighting for Delayed Labels
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Solution Overview
Problem
Machine learning models face challenges due to delayed user responses, leading to incomplete labels and suboptimal performance, as they often have to wait for significant periods to obtain label data, and there's no guarantee that all applicable events have been received and processed.
Innovation Solution
A system and method that modify training data by using importance weighting in the loss function, where scores generated by an initial machine-learned model are used to adjust the importance of training instances for a subsequent model, allowing for training without waiting for delayed user responses, thus addressing staleness and incomplete label issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system waits for delayed user responses to obtain complete label data, then the label completeness is improved, but the training time and model staleness increase
Solution Approach 1:
The system performs preliminary actions by generating synthetic negative labels immediately when user responses are delayed, rather than waiting for complete data. This allows training to proceed with partially labeled data, reducing training time while maintaining acceptable label completeness through the use of importance weighting to prioritize high-quality samples.
2Productivity
If unaccepted invitations are treated as negative labels immediately, then the training speed is improved, but the model accuracy deteriorates due to underestimated acceptance rates
Solution Approach 1:
The system changes parameters by introducing importance weighting that adjusts the contribution of each training sample based on its likelihood of being a true negative. This allows the model to train quickly on large datasets while correcting for the bias in negative label estimation through weighted sampling, thereby maintaining both training speed and accuracy.
Solution Approach 2:
The importance weight acts as an intermediary that mediates between the naive negative labeling approach and the ideal complete labeling scenario. It allows the system to use immediate negative labels for fast training while compensating for their potential inaccuracy through the weighting mechanism, thus bridging the gap between speed and precision.
3Device complexity
If the system uses naive negative labeling strategy, then the device complexity is reduced, but the model performance deteriorates
Solution Approach 1:
The system modifies a single key parameter - the loss function - by incorporating importance weights that adjust sample contributions. This minimal change to the training objective allows the system to maintain the simplicity of immediate negative labeling while significantly improving model performance by accounting for the delayed response bias, thus achieving better reliability with minimal increased complexity.
Data Source
AI summary
Techniques for tackling delayed user response by modifying training data for machine-learned models are provided. In one technique, a first machine-learned model generates a score based on a set of feature values. A training instance is generated based on the set of feature values. An attribute of the training instance is modified based on the score to generate a modified training instance. The attribute may be an importance weight of the training instance or a label of the training instance. The modified training instance is added to a training data. One or more machine learning techniques are used to train a second machine-learned model based on the training data.