1 code implementation • 31 May 2024 • Zhouxing Shi, Qirui Jin, Zico Kolter, Suman Jana, Cho-Jui Hsieh, huan zhang
Our framework also allows the verification of general nonlinear computation graphs and enables verification applications beyond simple neural networks, particularly for AC Optimal Power Flow (ACOPF).
1 code implementation • 11 Apr 2024 • Lujie Yang, Hongkai Dai, Zhouxing Shi, Cho-Jui Hsieh, Russ Tedrake, huan zhang
The flexibility and efficiency of our framework allow us to demonstrate Lyapunov-stable output feedback control with synthesized NN-based controllers and NN-based observers with formal stability guarantees, for the first time in literature.
1 code implementation • 26 Feb 2024 • Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh
The inferred prompt is called the backtranslated prompt which tends to reveal the actual intent of the original prompt, since it is generated based on the LLM's response and is not directly manipulated by the attacker.
no code implementations • 16 Nov 2023 • Yuhang Li, Yihan Wang, Zhouxing Shi, Cho-Jui Hsieh
In this work, we propose to improve the quality of texts generated by a watermarked language model by Watermarking with Importance Scoring (WIS).
2 code implementations • 31 May 2023 • Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, Cho-Jui Hsieh
The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users.
2 code implementations • 13 Oct 2022 • Zhouxing Shi, Yihan Wang, huan zhang, Zico Kolter, Cho-Jui Hsieh
In this paper, we develop an efficient framework for computing the $\ell_\infty$ local Lipschitz constant of a neural network by tightly upper bounding the norm of Clarke Jacobian via linear bound propagation.
no code implementations • ICLR 2022 • Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh
Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial perturbations present, while the convergence of IBP training remains unknown in existing literature.
1 code implementation • ACL 2022 • Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang
We propose two new criteria, sensitivity and stability, that provide complementary notions of faithfulness to the existed removal-based criteria.
2 code implementations • NeurIPS 2021 • Zhouxing Shi, Yihan Wang, huan zhang, JinFeng Yi, Cho-Jui Hsieh
Despite that state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, they usually use a long warmup schedule with hundreds or thousands epochs to reach SOTA performance and are thus still costly.
1 code implementation • 29 Mar 2021 • Rulin Shao, Zhouxing Shi, JinFeng Yi, Pin-Yu Chen, Cho-Jui Hsieh
Following the success in advancing natural language processing and understanding, transformers are expected to bring revolutionary changes to computer vision.
no code implementations • 7 Jan 2021 • Rulin Shao, Zhouxing Shi, JinFeng Yi, Pin-Yu Chen, Cho-Jui Hsieh
At the second stage, we design and apply a highly transferable adversarial attack for text CAPTCHAs to better obstruct CAPTCHA solvers.
no code implementations • 1 Jan 2021 • Jing Xu, Zhouxing Shi, huan zhang, JinFeng Yi, Cho-Jui Hsieh, LiWei Wang
We also demonstrate that the perturbation budget generator can produce semantically-meaningful budgets, which implies that the generator can capture contextual information and the sensitivity of different features in a given image.
no code implementations • 9 Jun 2020 • Mantong Zhou, Zhouxing Shi, Minlie Huang, Xiaoyan Zhu
During document retrieval, a candidate document is scored by considering its relationship to the question and other documents.
5 code implementations • NeurIPS 2020 • Kaidi Xu, Zhouxing Shi, huan zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh
Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense.
1 code implementation • ICLR 2020 • Zhouxing Shi, huan zhang, Kai-Wei Chang, Minlie Huang, Cho-Jui Hsieh
Robustness verification that aims to formally certify the prediction behavior of neural networks has become an important tool for understanding model behavior and obtaining safety guarantees.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Zhouxing Shi, Minlie Huang
Revealing the robustness issues of natural language processing models and improving their robustness is important to their performance under difficult situations.
1 code implementation • 1 Dec 2018 • Zhouxing Shi, Minlie Huang
This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues.
Ranked #6 on Discourse Parsing on STAC