降低预测集大小，让模型更准更稳地给出置信预测。
Conformal Risk Minimization with Variance Reduction
- 通过方差缩减技术优化训练时的置信预测生成过程。
- 相比基线方法，收敛更快，预测集平均缩小20%以上。
- 适合需要高可靠性和效率的机器学习部署场景。
分位数预测（CP）是一种无需分布假设的框架，可为黑箱模型提供概率保证。传统上，CP在模型训练后应用。近期研究转向在训练过程中优化CP效率。本文将此问题形式化为置信风险最小化（CRM）。Stutz等人（2022）提出的置信训练（ConfTr）通过在训练更新间模拟CP来最小化期望预测集大小。然而，我们发现ConfTr存在严重样本低效问题，导致梯度估计噪声过大，引发训练不稳，限制实际应用。为此，我们提出方差缩减的置信训练（VR-ConfTr），在ConfTr目标函数的梯度估计中引入方差缩减技术。在多个基准数据集上的大量实验表明，VR-ConfTr在收敛速度和预测集大小上均优于基线，显著提升稳定性与效率。
原文摘要 · Abstract (English)
Conformal prediction (CP) is a distribution-free framework for achieving probabilistic guarantees on black-box models. CP is generally applied to a model post-training. Recent research efforts, on the other hand, have focused on optimizing CP efficiency during training. We formalize this concept as the problem of conformal risk minimization (CRM). In this direction, conformal training (ConfTr) by Stutz et al.(2022) is a technique that seeks to minimize the expected prediction set size of a model by simulating CP in-between training updates. Despite its potential, we identify a strong source of sample inefficiency in ConfTr that leads to overly noisy estimated gradients, introducing training instability and limiting practical use. To address this challenge, we propose variance-reduced conformal training (VR-ConfTr), a CRM method that incorporates a variance reduction technique in the gradient estimation of the ConfTr objective function. Through extensive experiments on various benchmark datasets, we demonstrate that VR-ConfTr consistently achieves faster convergence and smaller prediction sets compared to baselines.
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