Missing values are common in photovoltaic (PV) power data, yet the uncertainty they induce is not propagated into predictive distributions. We develop a framework that incorporates missing-data uncertainty into short-term PV forecasting by combining stochastic multiple imputation with Rubin's rule. The approach is model-agnostic and can be integrated with standard machine-learning predictors. Empirical results show that ignoring missing-data uncertainty leads to overly narrow prediction intervals. Accounting for this uncertainty improves interval calibration while maintaining comparable point prediction accuracy. These results demonstrate the importance of propagating imputation uncertainty in data-driven PV forecasting.

To this end, high-probability guarantees have been considered in the literature [35, 64, 20, 32, 22]. These results allow to control the risk associated with the worst-case tail events as theyspecify howmanyiterations would be sufficient toensureG(xk,yk) issufficiently small foranygivenfailure probability q (0,1).

Manifold clustering is an important problem in motion and video segmentation, natural image clustering, and other applications where high-dimensional data lie on multiple, low-dimensional, nonlinear manifolds.

Overall, our method can generate high-fidelity, diverse, and multi-view consistent meshes from single-view wild images within 30 seconds, as shown in Figure 1. We conduct extensive experiments on various wild 2D images with different styles.

Their primary objective is to efficiently support the insertion of new elements with assigned priorities and the extraction of the highest priorityelement.

For iterative algorithms, implicit differentiation alleviates this issue but requires custom implementation of Jacobian evaluation. In this paper, we study one-step differentiation, also known as Jacobian-free backpropagation, a method as easy as automatic differentiation and as efficient as implicit differentiation for fast algorithms (e.g., superlinear

These models excel in zero-shot and few-shot learning, enabling them to learn new tasks without parameter updates.