With wide deployment of machine learning (ML)-based systems for a variety of applications including medical, military, automotive, genomic, multimedia, and social networking, there is …
C Dawson, S Gao, C Fan - IEEE Transactions on Robotics, 2023 - ieeexplore.ieee.org
Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics, but this performance comes at the cost of reduced …
Despite efforts to align large language models (LLMs) with human values, widely-used LLMs such as GPT, Llama, Claude, and PaLM are susceptible to jailbreaking attacks …
B Zhang, D Jiang, D He… - Advances in neural …, 2022 - proceedings.neurips.cc
Designing neural networks with bounded Lipschitz constant is a promising way to obtain certifiably robust classifiers against adversarial examples. However, the relevant progress …
In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work is …
K Leino, Z Wang, M Fredrikson - … Conference on Machine …, 2021 - proceedings.mlr.press
The threat of adversarial examples has motivated work on training certifiably robust neural networks to facilitate efficient verification of local robustness at inference time. We formalize …
L Li, T Xie, B Li - 2023 IEEE symposium on security and privacy …, 2023 - ieeexplore.ieee.org
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 …
Removing information from a machine learning model is a non-trivial task that requires to partially revert the training process. This task is unavoidable when sensitive data, such as …
K Kawaguchi, Z Deng, X Ji… - … Conference on Machine …, 2023 - proceedings.mlr.press
Numerous deep learning algorithms have been inspired by and understood via the notion of information bottleneck, where unnecessary information is (often implicitly) minimized while …