Li, Y., Yang, B. C., Jia, Y., Zhuang, D., & Mitra, S. (2023). Refining Perception Contracts: Case Studies in Vision-based Safe Auto-landing. arXiv preprint arXiv:2311.08652. (Thrust 2)
Kang, M., Song, D., & Li, B. (2023, November). DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification. In Thirty-seventh Conference on Neural Information Processing Systems. (Thrust 1)
Zhu, H., Li, Y., Shen, K., & Mitra, S. (2023, October). Parallel and Incremental Verification of Hybrid Automata with Ray and Verse. In International Symposium on Automated Technology for Verification and Analysis (pp. 95-114). Cham: Springer Nature Switzerland. (Thrust 2)
Dawson, C., & Fan, C. (2023). A Bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling. arXiv preprint arXiv:2309.08052. (Thrust 2)
Sharma, V., Zhao, P., & Hovakimyan, N. (2023). Learning Tube-Certified Control Using Robust Contraction Metrics. arXiv preprint arXiv:2309.07443. (Thrust 1)
Chen, Q., Cheng, S., & Hovakimyan, N. (2023). Simultaneous Spatial and Temporal Assignment for Fast UAV Trajectory Optimization using Bilevel Optimization. IEEE Robotics and Automation Letters. (Thrust 1)
Bates, I. W., Karimoddini, A., & Karimadini, M. (2022). A Learning-Based Approach for Diagnosis and Diagnosability of Unknown Discrete Event Systems. IEEE Transactions on Neural Networks and Learning Systems. (Thrust 3)
Garg, K., Dawson, C., Xu, K., Ornik, M., & Fan, C. (2023). Model-free Neural Fault Detection and Isolation for Safe Control. IEEE Control Systems Letters. (Thrust 2 and 3)
Li, Y., Zhu, H., Braught, K., Shen, K., & Mitra, S. (2023, July). Verse: A python library for reasoning about multi-agent hybrid system scenarios. In International Conference on Computer Aided Verification (pp. 351-364). Cham: Springer Nature Switzerland. (Thrust 2)
Zhao, P., Ghabcheloo, R., Cheng, Y., Abdi, H., & Hovakimyan, N. (2023). Convex Synthesis of Control Barrier Functions Under Input Constraints. IEEE Control Systems Letters. (Thrust 1)
Song, L., Zhao, P., Wan, N., & Hovakimyan, N. (2023, May). Safety embedded stochastic optimal control of networked multi-agent systems via barrier states. In 2023 American Control Conference (ACC) (pp. 2554-2559). IEEE (Thrust 1)
Song, L., Li, Y., Cheng, S., Zhao, P., Mitra, S., & Hovakimyan, N. (2023, May). Verification of ℒ1 Adaptive Control using Verse Library: A Case Study of Quadrotors. In Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023) (pp. 245-246). (Thrust 1 and 2)
Wu, Z., Cheng, S., Zhao, P., Gahlawat, A., Ackerman, K. A., Lakshmanan, A., … & Hovakimyan, N. (2023). L 1 quad: L 1 Adaptive Augmentation of Geometric Control for Agile Quadrotors With Performance Guarantees. arXiv preprint arXiv:2302.07208. (Thrust 1)
Song, L., Wan, N., & Hovakimyan, N. (2023). Safe Optimal Control with Synthesized Waypoints as Guidance. In AIAA SCITECH 2023 Forum (p. 0880). (Thrust 1)
El-Kebir, H., Berlin, R., Bentsman, J., & Ornik, M. (2023). Robustly Linearized Model Predictive Control for Nonlinear Infinite-Dimensional Systems. In Proceedings of the 22nd World Congress of the International Federation of Automatic Control. (Thrust 3)
Cheng, Y., Zhao, P., & Hovakimyan, N. (2023, June). Safe and Efficient Reinforcement Learning using Disturbance-Observer-Based Control Barrier Functions. In Learning for Dynamics and Control Conference (pp. 104-115). PMLR. (Thrust 1)
Cheng, S., Song, L., Kim, M., Wang, S., & Hovakimyan, N. (2023, June). DiffTune $^+ $: Hyperparameter-Free Auto-Tuning using Auto-Differentiation. In Learning for Dynamics and Control Conference (pp. 170-183). PMLR. (Thrust 1)
Liu, Z., Guo, Z., Cen, Z., Zhang, H., Tan, J., Li, B., & Zhao, D. (2022). On the robustness of safe reinforcement learning under observational perturbations. arXiv preprint arXiv:2205.14691. (Thrust 1)
Xu, M., Huang, P., Niu, Y., Kumar, V., Qiu, J., Fang, C., … & Zhao, D. (2023, April). Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables. In International Conference on Artificial Intelligence and Statistics (pp. 2677-2703). PMLR. (Thrust 1)
Ding, W., Lin, H., Li, B., Eun, K. J., & Zhao, D. (2021). Semantically adversarial driving scenario generation with explicit knowledge integration. arXiv preprint arXiv:2106.04066. (Thrust 1)
Generation, S. C. D. S. CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation. (Thrust 1)