论文标题
使用最大平均差异校准可靠回归
Calibrated Reliable Regression using Maximum Mean Discrepancy
论文作者
论文摘要
不确定性的准确量化对于机器学习的现实应用至关重要。但是，现代深层神经网络仍然会产生不可靠的预测不确定性，通常会产生过度自信的预测。在本文中，我们关注的是在回归任务中获得良好的预测。我们通过最小化内核嵌入度量来使用最大平均差异提出校准回归方法。从理论上讲，当样本量足够大时，我们方法的校准误差会渐近地收敛到零。非平凡的真实数据集的实验表明，我们的方法可以产生精心校准且清晰的预测间隔，从而超过相关的最新方法。
Accurate quantification of uncertainty is crucial for real-world applications of machine learning. However, modern deep neural networks still produce unreliable predictive uncertainty, often yielding over-confident predictions. In this paper, we are concerned with getting well-calibrated predictions in regression tasks. We propose the calibrated regression method using the maximum mean discrepancy by minimizing the kernel embedding measure. Theoretically, the calibration error of our method asymptotically converges to zero when the sample size is large enough. Experiments on non-trivial real datasets show that our method can produce well-calibrated and sharp prediction intervals, which outperforms the related state-of-the-art methods.
