4 citations · 8 across the 9 of their papers we have counts for
10 papers
A Measure-Geometric Theory of Robustness in Machine Learning
Cheng-Chao Huang
This paper develops a foundational measure-geometric theory of robustness in machine learning. Robustness is formulated as escape from a retained task state relative to a reference…
Synthesizing Probabilistic Saturating Counters with Differentially Private Formal Guarantees
Zhiming Chi, Lutan Zhao, Depeng Liu +8
Branch predictors improve instruction-level parallelism in modern processors and are commonly modeled using saturating counters. However, classical saturating counters are determin…
TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction
Liang Zhang, Gaojie Jin, Yao Shi +4
The robustness of trajectory prediction models is crucial for developing safe autonomous driving systems. Adversarial attacks on trajectory prediction can significantly impair the…
Patch Synthesis for Property Repair of Deep Neural Networks
Zhiming Chi, Jianan Ma, Pengfei Yang +4
Deep neural networks (DNNs) are prone to various dependability issues, such as adversarial attacks, which hinder their adoption in safety-critical domains. Recently, NN repair tech…
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models
Liang Zhang, Nathaniel Xu, Pengfei Yang +3
Robust pedestrian trajectory forecasting is crucial to developing safe autonomous vehicles. Although previous works have studied adversarial robustness in the context of trajectory…
Incremental Satisfiability Modulo Theory for Verification of Deep Neural Networks
Pengfei Yang, Zhiming Chi, Zongxin Liu +4
Constraint solving is an elementary way for verification of deep neural networks (DNN). In the domain of AI safety, a DNN might be modified in its structure and parameters for its…
Safety Analysis of Autonomous Driving Systems Based on Model Learning
Renjue Li, Tianhang Qin, Pengfei Yang +3
We present a practical verification method for safety analysis of the autonomous driving system (ADS). The main idea is to build a surrogate model that quantitatively depicts the b…
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
Renjue Li, Hanwei Zhang, Pengfei Yang +4
In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks. The framework builds an ensemble of sub-models -…
Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning
Renjue Li, Pengfei Yang, Cheng-Chao Huang +3
To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with sc…
Improving Neural Network Verification through Spurious Region Guided Refinement
Pengfei Yang, Renjue Li, Jianlin Li +5
We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze…