In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …

Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

Deep learning models for natural language processing (NLP) are increasingly adopted and
deployed by analysts without formal training in NLP or machine learning (ML). However, the …

Deep learning has transformed the way we think of software and what it can do. But deep
neural networks are fragile and their behaviors are often surprising. In many settings, we …

Randomized smoothing is a leading approach for constructing classifiers that are certifiably
robust against adversarial examples. Existing work on randomized smoothing has focused …

Machine Learning (ML) has exhibited substantial success in the field of Natural Language
Processing (NLP). For example large language models have empirically proven to be …

With the widespread deployment of deep neural networks (DNNs), ensuring the reliability of
DNN-based systems is of great importance. Serious reliability issues such as system failures …

Recent years have witnessed a surge of certified robust training pipelines against text
adversarial perturbation constructed by synonym substitutions. Given a base model, existing …