Regression with Rejection: No-Rejection Learning Consistency Study

핵심 개념

No-rejection learning strategy in regression with rejection ensures consistency and performance improvement.

초록

Abstract: Learning with rejection model for human-AI interaction on prediction tasks. Challenges of non-convexity and inconsistency in regression with rejection. Introduction: Problem of learning with rejection: predictor and rejector components. Importance of rejector in focusing predictor on high-confidence samples. Contributions: Establishing consistency under weak realizability condition. Introducing truncated loss for rejector calibration. Related Literature: Comparison with existing works on regression with rejection. Consistency under Realizability: Challenges of joint learning of regressor and rejector. Two-step no-rejection learning procedure for consistency. Learning with Weak Realizability: Formalizing weak realizability condition for consistent learning. No-rejection learning as a consistent approach. Surrogate Property: Truncated loss as a proxy for the original loss. Squared loss as a surrogate for the truncated loss. Error Bounds for No-Rejection Learning: Theoretical error bounds for no-rejection learning. Learning the Rejector: Algorithm for regressor-agnostic rejector learning. Approach for rejector learning with fixed budget. Numerical Experiments: Performance comparison of algorithms in fixed-cost and fixed-budget settings.

통계

"The model NN+kNNRej has a uniform advantage over the other algorithms across different datasets." "SelNet with α = 0.5 generally performs better than SelNet with α = 1 in the fixed-budget setting."

인용구

"No-rejection learning strategy ensures consistency and performance improvement in regression with rejection." "The rejector can be viewed as a binary classifier assigning prediction tasks to predictor or human." "Truncated loss serves as a proxy for the original loss, with the squared loss as a surrogate."

핵심 통찰 요약

When No-Rejection Learning is Consistent for Regression with Rejection

by Xiaocheng Li... 게시일 arxiv.org 03-05-2024

https://arxiv.org/pdf/2307.02932.pdf

더 깊은 질문

How can the concept of no-rejection learning be applied to other machine learning tasks

No-rejection learning can be applied to various machine learning tasks where the rejection option is available. For example, in classification tasks with rejection, the concept of no-rejection learning can be utilized to train the classifier using all available data points, rather than excluding rejected samples. This approach can lead to improved model performance by leveraging information from all data points, even those initially deemed uncertain or low confidence. Similarly, in anomaly detection tasks, no-rejection learning can be beneficial in training anomaly detection models to make predictions on all data instances, including those that were initially flagged as anomalies. By incorporating all data points into the training process, the model can learn more effectively and potentially achieve better generalization performance.

What are the implications of the weak realizability condition on the generalization of learning algorithms

The weak realizability condition has significant implications on the generalization of learning algorithms. When the weak realizability condition holds, it ensures that the function class includes the conditional expectation function, allowing for consistency in learning algorithms. This condition guarantees that the sub-optimality of no-rejection learning only arises when the underlying function class is not rich enough. In terms of generalization, the weak realizability condition enables the development of consistent surrogate losses and provides a theoretical foundation for no-rejection learning strategies. Algorithms that satisfy the weak realizability condition are more likely to generalize well to unseen data and exhibit robust performance across different datasets.

How can the rejector calibration algorithm be further optimized for efficiency and accuracy

To optimize the rejector calibration algorithm for efficiency and accuracy, several strategies can be implemented. Firstly, incorporating advanced machine learning techniques such as ensemble methods or deep learning architectures can enhance the calibration accuracy of the rejector. These methods can capture complex patterns in the data and improve the precision of the rejector's predictions. Additionally, optimizing the hyperparameters of the calibration algorithm, such as the threshold values or the choice of the score function, can fine-tune the rejector's performance. Furthermore, leveraging techniques from statistical learning theory, such as cross-validation or bootstrapping, can help validate the calibration algorithm and ensure its reliability on unseen data. By iteratively refining the calibration process and experimenting with different approaches, the rejector calibration algorithm can be optimized for both efficiency and accuracy.