Mathematical optimization is one of the cornerstones of modern engineering research and
practice. Yet, throughout all application domains, mathematical optimization is, for the most …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but
as LMs grow in size, backpropagation requires a prohibitively large amount of memory …

Current model extraction attacks assume that the adversary has access to a surrogate
dataset with characteristics similar to the proprietary data used to train the victim model. This …

Learning to classify time series with limited data is a practical yet challenging problem.
Current methods are primarily based on hand-designed feature extraction rules or domain …

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to
numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient …

The deployment constraints in practical applications necessitate the pruning of large-scale
deep learning models, ie, promoting their weight sparsity. As illustrated by the Lottery Ticket …

Although deep learning has made great progress in recent years, the exploding economic
and environmental costs of training neural networks are becoming unsustainable. To …

To cope with increasing uncertainty from renewable generation and flexible load, grid
operators need to solve alternative current optimal power flow (AC-OPF) problems more …

Abstract Machine learning classifiers are critically prone to evasion attacks. Adversarial
examples are slightly modified inputs that are then misclassified, while remaining …