Abstract We propose Multivariate Quantile Function Forecaster (MQF2), a global
probabilistic forecasting method constructed using a multivariate quantile function and …

Quantile regression is an effective technique to quantify uncertainty, fit challenging
underlying distributions, and often provide full probabilistic predictions through joint …

Large-scale datasets have played a crucial role in the advancement of computer vision.
However, they often suffer from problems such as class imbalance, noisy labels, dataset …

Random reshuffling, which randomly permutes the dataset each epoch, is widely adopted in
model training because it yields faster convergence than with-replacement sampling …

Probabilistic time series forecasting has played critical role in decision-making processes
due to its capability to quantify uncertainties. Deep forecasting models, however, could be …

Mutation testing is vital for ensuring software quality. However, the presence of equivalent
mutants is known to introduce redundant cost and bias issues, hindering the effectiveness of …

Training example order in SGD has long been known to affect convergence rate. Recent
results show that accelerated rates are possible in a variety of cases for permutation-based …

Federated learning (FL) is a promising collaborative learning approach in edge computing,
reducing communication costs and addressing the data privacy concerns of traditional cloud …

Recent research on online Gradient Balancing (GraB) has revealed that there exist
permutation-based example orderings for SGD that are guaranteed to outperform random …

The world is not static: This causes real-world time series to change over time through
external, and potentially disruptive, events such as macroeconomic cycles or the COVID-19 …