Safe and Trustworthy Machine Learning

Executive Summary

Through this project we will introduce the notion of certified safety in machine learning systems by developing models with guaranteed robustness and designing a suite of statistical methods to reliably examine and debug trained models. By making a fundamental advance in the field of machine learning, this research will have far-reaching impact across many national security applications, which rely increasingly on artificial intelligence.

Publications, Presentations, and Patents

Diffenderfer, James, and Bhavya Kailkhura. "Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network." International Conference on Learning Representations. 2020.

Pan, Boyuan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, and Bo Li. "Adversarial Mutual Information for Text Generation." In International Conference on Machine Learning, pp. 7476-7486. PMLR, 2020.

Gokhale, Tejas, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, and Yezhou Yang. "Attribute-Guided Adversarial Training for Robustness to Natural Perturbations." Thirty-Fourth AAAI Conference on Artificial Intelligence. 2020.

Xu, Kaidi, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, and Cho-Jui Hsieh. "Automatic perturbation analysis for scalable certified robustness and beyond." Advances in Neural Information Processing Systems 33. 2020.

Bulusu, Saikiran, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, and Dawn Song. "Anomalous example detection in deep learning: A survey." IEEE Access 8: 132330-132347. 2020.

Liu, Sijia, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred O. Hero III, and Pramod K. Varshney. "A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications." IEEE Signal Processing Magazine 37, no. 5: 43-54. 2020.

Li, Linyi, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, and Bo Li. "Tss: Transformation-specific smoothing for robustness certification." To appear in ACM Conference on Computer and Communications Security. 2021.

Mehra, Akshay, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm. "How Robust are Randomized Smoothing based Defenses to Data Poisoning?." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13244-13253. 2021.

Jia, Ruoxi, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, and Dawn Song. "Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification?." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8239-8247. 2021.