标题： 图结构反馈多模型集成在线同构预测 显示英文标题

标题： Graph-Structured Feedback Multimodel Ensemble Online Conformal Prediction

摘要： 在线共形预测已展现出其能力，可以为每个到来的数据点构建一个预测集，该预测集以预定的概率覆盖真实标签。 为了应对潜在的分布变化，多模型在线共形预测被引入，以从预选的候选集选择和利用不同的模型。 随着灵活性的提高，候选集的选择也带来了挑战。 一个包含大量模型的候选集可能会增加计算复杂度。 此外，包含无关模型或性能较差的模型可能对性能产生负面影响，并导致不必要的大的预测集。 为了解决这些挑战，我们提出了一种新颖的多模型在线共形预测算法，该算法通过从二部图中收集反馈，在接收到新数据时对其进行优化，从而在每个时间步识别出有效的模型子集。 然后从这个子集中选择一个模型来构建预测集，从而减少计算复杂度并得到更小的预测集。 此外，我们证明了使用预测集大小作为反馈，结合模型损失，可以通过构建更小的预测集显著提高效率，同时仍满足所需的覆盖率保证。 所提出的算法被证明可以确保有效的覆盖率并实现次线性遗憾。 在真实和合成数据集上的实验验证了所提出的方法能够构建更小的预测集，并优于现有的多模型在线共形预测方法。 显示英文摘要

摘要： Online conformal prediction has demonstrated its capability to construct a prediction set for each incoming data point that covers the true label with a predetermined probability. To cope with potential distribution shift, multi-model online conformal prediction has been introduced to select and leverage different models from a preselected candidate set. Along with the improved flexibility, the choice of the preselected set also brings challenges. A candidate set that includes a large number of models may increase the computational complexity. In addition, the inclusion of irrelevant models with poor performance may negatively impact the performance and lead to unnecessarily large prediction sets. To address these challenges, we propose a novel multi-model online conformal prediction algorithm that identifies a subset of effective models at each time step by collecting feedback from a bipartite graph, which is refined upon receiving new data. A model is then selected from this subset to construct the prediction set, resulting in reduced computational complexity and smaller prediction sets. Additionally, we demonstrate that using prediction set size as feedback, alongside model loss, can significantly improve efficiency by constructing smaller prediction sets while still satisfying the required coverage guarantee. The proposed algorithms are proven to ensure valid coverage and achieve sublinear regret. Experiments on real and synthetic datasets validate that the proposed methods construct smaller prediction sets and outperform existing multi-model online conformal prediction approaches.