1.3k citations · 2.7k across the 38 of their papers we have counts for
12 papers · 1 filter
Exploiting Code Symmetries for Learning Program Semantics
Kexin Pei, Weichen Li, Qirui Jin +5
This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture. We in…
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Huan Zhang, Shiqi Wang, Kaidi Xu +5
Bound propagation methods, when combined with branch and bound, are among the most effective methods to formally verify properties of deep neural networks such as correctness, robu…
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification
Shiqi Wang, Huan Zhang, Kaidi Xu +4
Bound propagation based incomplete neural network verifiers such as CROWN are very efficient and can significantly accelerate branch-and-bound (BaB) based complete verification of…
Ensuring Fairness Beyond the Training Data
Debmalya Mandal, Samuel Deng, Suman Jana +2
We initiate the study of fair classifiers that are robust to perturbations in the training distribution. Despite recent progress, the literature on fairness has largely ignored the…
Towards Understanding Fast Adversarial Training
Bai Li, Shiqi Wang, Suman Jana +1
Current neural-network-based classifiers are susceptible to adversarial examples. The most empirically successful approach to defending against such adversarial examples is adversa…
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Bai Li, Shiqi Wang, Yunhan Jia +4
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the orig…
CLN2INV: Learning Loop Invariants with Continuous Logic Networks
Gabriel Ryan, Justin Wong, Jianan Yao +2
Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the…
Towards Compact and Robust Deep Neural Networks
Vikash Sehwag, Shiqi Wang, Prateek Mittal +1
Deep neural networks have achieved impressive performance in many applications but their large number of parameters lead to significant computational and storage overheads. Several…
Enhancing Gradient-based Attacks with Symbolic Intervals
Shiqi Wang, Yizheng Chen, Ahmed Abdou +1
Recent breakthroughs in defenses against adversarial examples, like adversarial training, make the neural networks robust against various classes of attackers (e.g., first-order gr…
MixTrain: Scalable Training of Verifiably Robust Neural Networks
Shiqi Wang, Yizheng Chen, Ahmed Abdou +1
Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks. The most promising defenses, adversarially robust training a…
Efficient Formal Safety Analysis of Neural Networks
Shiqi Wang, Kexin Pei, Justin Whitehouse +2
Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these netw…
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Kexin Pei, Yinzhi Cao, Junfeng Yang +1
Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictab…