Miaoxuan Zhu

Miaoxuan Zhu, Yi Yu, Yuyang Li et al.

The quantification of uncertainty in prediction models is crucial for reliable decision-making, yet remains a significant challenge. Interval time series forecasting offers a principled solution to this problem by providing prediction intervals (PIs), which indicates the probability that the true value falls within the predicted range. We consider a recently established point forecasts (PFs) method termed Learning-Based Convolution Nuclear Norm Minimization (LbCNNM), which directly generates multi-step ahead forecasts by leveraging the convolutional low-rankness property derived from training data. While theoretically complete and empirically effective, LbCNNM lacks inherent uncertainty estimation capabilities, a limitation shared by many advanced forecasting methods. To resolve the issue, we modify the well-known Quantile Regression (QR) and integrate it into LbCNNM, resulting in a novel interval forecasting method termed LbCNNM with Modified Quantile Regression (LbCNNM-MQR). In addition, we devise interval calibration techniques to further improve the accuracy of PIs. Extensive experiments on over 100,000 real-world time series demonstrate the superior performance of LbCNNM-MQR.

Kaile Du, Zihan Ye, Junzhou Xie et al.

Prompt-based methods have shown strong effectiveness in single-label class-incremental learning, but their direct extension to multi-label class-incremental learning (MLCIL) performs poorly due to two intrinsic challenges: semantic confusion from co-occurring categories and true-negative-false-positive confusion caused by partial labeling. We propose Dual-Decoupled Prompting (DDP), a replay-free and parameter-efficient framework that explicitly addresses both issues. DDP assigns class-specific positive-negative prompts to disentangle semantics and introduces Progressive Confidence Decoupling (PCD), a curriculum-inspired decoupling strategy that suppresses false positives. Past prompts are frozen as knowledge anchors, and interlayer prompting enhances efficiency. On MS-COCO and PASCAL VOC, DDP consistently outperforms prior methods and is the first replay-free MLCIL approach to exceed 80% mAP and 70% F1 under the standard MS-COCO B40-C10 benchmark.