Ranking Distillation With Quantile Loss for Student ML Models
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Solution Overview
Problem
Training complex machine learning models like deep neural networks is computationally intensive, making it infeasible on resource-constrained devices such as mobile phones or personal computers, and existing models often misspecify objectives leading to suboptimal performance in recommender systems.
Innovation Solution
Implementing knowledge distillation techniques using quantile and L1 regression to transfer knowledge from a computationally expensive teacher model to a less resource-intensive student model, focusing on ranking distillation to improve ranking accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models (deep neural networks) are trained to achieve high prediction accuracy, then prediction accuracy is improved, but computational complexity increases making it infeasible on resource-constrained devices
Solution Approach 1:
The patent creates a simplified student model that copies the ranking behavior of a complex teacher model through knowledge distillation. The student model learns to replicate the teacher's score differences by minimizing quantile loss on paired training examples, enabling deployment on resource-constrained devices while maintaining ranking performance.
Solution Approach 2:
The patent extracts only the essential ranking knowledge from the complex teacher model, specifically the score differences between paired examples, and transfers this distilled information to the student model. This extraction approach eliminates unnecessary computational complexity while preserving the core ranking capability needed for resource-constrained environments.
2Device complexity
If knowledge distillation is used to transfer from teacher model to student model, then computational feasibility is improved, but ranking accuracy may deteriorate due to model misspecification
Solution Approach 1:
The patent changes the loss function parameter from traditional L2 to quantile loss with multiple quantile levels (e.g., 0.1, 0.3, 0.7, 0.9). This parameter change makes the distillation process more robust to model misspecification by considering multiple quantiles of the score difference distribution, thereby maintaining ranking accuracy even when models are misspecified.
Solution Approach 2:
The patent combines multiple quantile losses at different levels into a composite loss function for training the student model. This composite approach integrates information from multiple quantiles (0.1, 0.3, 0.7, 0.9) to create a more robust training objective that preserves ranking accuracy despite the simplification from teacher to student model.
3Ease of operation
If traditional L2 loss is used for distillation, then training simplicity is maintained, but ranking performance deteriorates when models are misspecified
Solution Approach 1:
The patent changes the loss function from traditional L2 to quantile loss with multiple quantile levels. This parameter change maintains training simplicity through a straightforward minimization process while significantly improving ranking performance by making the distillation robust to model misspecification through multi-quantile consideration.
4Reliability
If quantile regression with multiple quantiles is used for distillation, then ranking robustness is improved, but training computational requirements increase
Solution Approach 1:
The patent segments the training process by using paired training examples (positive pairs and negative pairs) for each quantile level. This segmentation allows efficient computation of quantile losses by processing examples in structured pairs, reducing the overall training computational burden while maintaining ranking robustness through multi-quantile training.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that obtain a first machine learning model that is configured to output a score. The training examples can each include feature values that represent features of an item, and an outcome label for the item. From the training examples, training pairs of training examples are determined. For each training pair: (i) a score is generated for each training example in the training pair using the first machine learning model; and (ii) for the training pair, a score difference of the scores generated for the training examples in the training pair is determined. Using the training pairs and the score differences, a second machine learning model is trained to produce score differences that, for the same training examples, are within a threshold value of the score differences produced by the first machine learning model.