Abstract Universal Approximation for Neural Networks. - Zi Wang 016; Aws Albarghouthi; Somesh Jha (CoRR, 2020)

Exploring Adversarial Robustness of Deep Metric Learning. - Thomas Kobber Panum; Zi Wang 016; Pengyu Kan; Earlence Fernandes; Somesh Jha (CoRR, 2021)

A Quantitative Geometric Approach to Neural-Network Smoothness. - Zi Wang 016; Gautam Prakriya; Somesh Jha (NeurIPS, 2022)

Semantic Robustness of Models of Source Code. - Jordan Henkel; Goutham Ramakrishnan; Zi Wang 016; Aws Albarghouthi; Somesh Jha; Thomas W. Reps (SANER, 2022)

Interval universal approximation for neural networks. - Zi Wang 016; Aws Albarghouthi; Gautam Prakriya; Somesh Jha (Proc. ACM Program. Lang., 2022)

Efficient Symbolic Reasoning for Neural-Network Verification. - Zi Wang 016; Somesh Jha; Krishnamurthy Dvijotham (CoRR, 2023)

Rethink Diversity in Deep Learning Testing. - Zi Wang 016; Jihye Choi; Somesh Jha (CoRR, 2023)

On the Scalability and Memory Efficiency of Semidefinite Programs for Lipschitz Constant Estimation of Neural Networks. - Zi Wang 016; Bin Hu 002; Aaron J. Havens; Alexandre Araujo; Yang Zheng 001; Yudong Chen; Somesh Jha (ICLR, 2024)

Functional Homotopy: Smoothing Discrete Optimization via Continuous Parameters for LLM Jailbreak Attacks. - Zi Wang 016; Divyam Anshumaan; Ashish Hooda; Yudong Chen; Somesh Jha (ICLR, 2025)

PEA: Enhancing LLM Performance on Computational-Reasoning Tasks. - Zi Wang 016; Shiwei Weng; Mohannad Alhanahnah; Somesh Jha; Thomas W. Reps (CoRR, 2025)