Joint ML Training With Action-Aware Cost Optimization
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Solution Overview
Problem
Conventional machine learning (ML) models often treat prediction and improvement processes separately, leading to inefficiencies and the need for additional training or retraining when results are unsatisfactory, without effectively incorporating downstream decision-making actions into the training process.
Innovation Solution
A joint prediction and improvement framework that integrates actions affected by ML model outputs into the training process, using a joint weighted cost function to optimize both predictive and task-defined objectives, such as through stochastic gradient descent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prediction and improvement processes are treated separately, then the ML model can be trained independently, but additional training or retraining is needed when results are unsatisfactory, reducing efficiency
Solution Approach 1:
The patent merges the prediction process and improvement process into a unified joint training framework. The joint weighted cost function combines both prediction accuracy objectives and task improvement objectives, allowing the ML model to be trained simultaneously on both goals. This eliminates the need for separate training stages and subsequent retraining, directly resolving the efficiency loss and time waste identified in the contradiction.
2Measurement precision
If conventional ML models focus only on prediction accuracy, then the model can be trained with standard loss functions, but downstream decision-making actions are not optimized, reducing relevance
Solution Approach 1:
The patent introduces a joint weighted cost function that changes the optimization parameters from traditional prediction-only loss functions to a composite function incorporating both prediction accuracy metrics and task improvement metrics. This parameter transformation allows the model to simultaneously optimize for accurate predictions and reliable downstream decisions, resolving the contradiction between measurement precision and reliability.
3Ease of manufacture
If separate training processes are used for prediction and improvement, then each can be optimized independently, but additional data and resources are required, increasing complexity
Solution Approach 1:
The patent creates a universal joint training framework that serves multiple functions simultaneously: it trains the ML model for accurate predictions while also optimizing downstream task performance. The joint weighted cost function acts as a multi-functional objective that consolidates what would otherwise require separate training processes, reducing overall process complexity while maintaining ease of training through a unified approach.
Data Source
AI summary
Methods, systems, and computer program products for a joint prediction and improvement framework for machine learning models are provided herein. A method includes obtaining a machine learning model initialized with a set of parameters; identifying one or more actions based on test inputs corresponding to the machine learning model and historical actions related to a task, where the historical actions are dependent on respective historical outputs of the machine learning model; using the identified one or more actions to jointly compute: one or more first values corresponding to inference loss for the machine learning model; and one or more second values based at least in part on a computing cost function associated with the task; and updating the set of parameters of the machine learning model based on the one or more first values and the one or more second values.