Kernel Adaptive Filtering Multiple-model Actuator Fault Diagnostic For Multi-effectors Aircraft. - Peng Zhu; Wenhan Dong; Yuhao Mao; Haoyu Shi; Xiaoshan Ma (ASCC, 2019)

A Framework for Enhancing Deep Learning Based Recommender Systems with Knowledge Graphs. - Sudhir P. Mudur; Serguei A. Mokhov; Yuhao Mao (IDEAS, 2021)

Forecasting of Vessel Traffic Flow Using BPNN Based on Genetic Algorithm Optimization. - Qihang Yi; Yi Zuo; Tieshan Li; Yuhao Mao; Yang Xiao 001 (IWCMC, 2021)

Adaptive Containment Control for Unmanned Surface Vessels. - Yuhao Mao; Qihe Shan; Tieshan Li; Shun Yang; Qihang Yi (SPAC, 2021)

Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy. - Yuhao Mao; Serguei A. Mokhov; Sudhir P. Mudur (CoRR, 2021)

"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution. - Yuyou Gan; Yuhao Mao; Xuhong Zhang 002; Shouling Ji; Yuwen Pu; Meng Han; Jianwei Yin; Ting Wang 006 (CCS, 2022)

Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings. - Yuhao Mao; Chong Fu 002; Saizhuo Wang; Shouling Ji; Xuhong Zhang 002; Zhenguang Liu; Jun Zhou 011; Alex X. Liu; Raheem Beyah; Ting Wang 006 (SP, 2022)

Connecting Certified and Adversarial Training. - Yuhao Mao; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (NeurIPS, 2023)

TAPS: Connecting Certified and Adversarial Training. - Yuhao Mao; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (CoRR, 2023)

SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment. - Zekun Qiu; Zhipu Xie; Zehua Ji; Yuhao Mao; Ke Cheng (CoRR, 2023)

Amplitude and Phase Errors Optimization for High-Precision Electrical Impedance Measurement System. - Lingfeng Luan; Yuhao Mao; Wenhao Zhang; Jieming Ni; Long Xu; Boxiao Liu (BioCAS, 2024)

Expressivity of ReLU-Networks under Convex Relaxations. - Maximilian Baader; Mark Niklas Müller; Yuhao Mao; Martin T. Vechev (ICLR, 2024)

Understanding Certified Training with Interval Bound Propagation. - Yuhao Mao; Mark Niklas Müller; Marc Fischer 002; Martin T. Vechev (ICLR, 2024)

SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment. - Zekun Qiu; Zhipu Xie; Zehua Ji; Yuhao Mao; Ke Cheng 003 (ICMLT, 2024)

Repeat-Aware Neighbor Sampling for Dynamic Graph Learning. - Tao Zou 003; Yuhao Mao; Junchen Ye; Bowen Du 001 (KDD, 2024)

Overcoming the Paradox of Certified Training with Gaussian Smoothing. - Stefan Balauca; Mark Niklas Müller; Yuhao Mao; Maximilian Baader; Marc Fischer 002; Martin T. Vechev (CoRR, 2024)

CTBENCH: A Library and Benchmark for Certified Training. - Yuhao Mao; Stefan Balauca; Martin T. Vechev (CoRR, 2024)

Multi-Neuron Unleashes Expressivity of ReLU Networks Under Convex Relaxation. - Yuhao Mao; Yani Zhang; Martin T. Vechev (CoRR, 2024)

Average Certified Radius is a Poor Metric for Randomized Smoothing. - Chenhao Sun; Yuhao Mao; Mark Niklas Müller; Martin T. Vechev (CoRR, 2024)

Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations. - Stefan Balauca; Mark Niklas Müller; Yuhao Mao; Maximilian Baader; Marc Fischer 002; Martin T. Vechev (Trans. Mach. Learn. Res., 2025)

HGSMAP: a novel heterogeneous graph-based associative percept framework for scenario-based optimal model assignment. - Zekun Qiu; Zhipu Xie; Zehua Ji; Yuhao Mao; Ke Cheng 003 (Knowl. Inf. Syst., 2025)

On the Security Risks of ML-based Malware Detection Systems: A Survey. - Ping He; Yuhao Mao; Changjiang Li; Lorenzo Cavallaro; Ting Wang 006; Shouling Ji (CoRR, 2025)