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 …

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 …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

We propose a framework for planning in unknown dynamic environments with probabilistic
safety guarantees using conformal prediction. Particularly, we design a model predictive …

Deep neural networks (DNNs) have demonstrated their outperformance in various domains.
However, it raises a social concern whether DNNs can produce reliable and fair decisions …

Neural networks have become increasingly popular. Nevertheless, understanding their
decision process turns out to be complicated. One vital method to explain a models' …

The widespread adoption of the Android operating system has made malicious Android
applications an appealing target for attackers. Machine learning-based (ML-based) Android …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Bound propagation with branch-and-bound (BaB) is so far among the most effective
methods for neural network (NN) verification. However, existing works with BaB have mostly …