Alexander Robey

According to our database, Alexander Robey authored at least 27 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Jailbreaking LLM-Controlled Robots.

Alexander Robey, Zachary Ravichandran, Vijay Kumar, Hamed Hassani, George J. Pappas

CoRR, 2024

JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models.

Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong

CoRR, 2024

Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation.

Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, J. Zico Kolter

CoRR, 2024

A Safe Harbor for AI Evaluation and Red Teaming.

Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson

CoRR, 2024

Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing.

Jiabao Ji, Bairu Hou, Alexander Robey, George J. Pappas, Hamed Hassani, Yang Zhang, Eric Wong, Shiyu Chang

CoRR, 2024

Position: A Safe Harbor for AI Evaluation and Red Teaming.

Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Adversarial Training Should Be Cast as a Non-Zero-Sum Game.

Alexander Robey, Fabian Latorre, George J. Pappas, Hamed Hassani, Volkan Cevher

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Provable Tradeoffs in Adversarially Robust Classification.

Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey

IEEE Trans. Inf. Theory, December, 2023

Data-Driven Modeling and Verification of Perception-Based Autonomous Systems.

Thomas Waite, Alexander Robey, Hamed Hassani, George J. Pappas, Radoslav Ivanov

CoRR, 2023

Jailbreaking Black Box Large Language Models in Twenty Queries.

Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong

CoRR, 2023

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas

CoRR, 2023

Toward Certified Robustness Against Real-World Distribution Shifts.

Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George J. Pappas, Hamed Hassani, Corina S. Pasareanu, Clark W. Barrett

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

2022

Probable Domain Generalization via Quantile Risk Minimization.

Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Sample Complexity of Stability Constrained Imitation Learning.

Stephen Tu, Alexander Robey, Tingnan Zhang, Nikolai Matni

Proceedings of the Learning for Dynamics and Control Conference, 2022

Probabilistically Robust Learning: Balancing Average and Worst-case Performance.

Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani

Proceedings of the International Conference on Machine Learning, 2022

Do deep networks transfer invariances across classes?

Allan Zhou, Fahim Tajwar, Alexander Robey, Tom Knowles, George J. Pappas, Hamed Hassani, Chelsea Finn

Proceedings of the Tenth International Conference on Learning Representations, 2022

Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks.

Anton Xue, Lars Lindemann, Alexander Robey, Hamed Hassani, George J. Pappas, Rajeev Alur

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021

Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations.

Lars Lindemann, Alexander Robey, Lejun Jiang, Stephen Tu, Nikolai Matni

CoRR, 2021

Closing the Closed-Loop Distribution Shift in Safe Imitation Learning.

Stephen Tu, Alexander Robey, Nikolai Matni

CoRR, 2021

Model-Based Domain Generalization.

Alexander Robey, George J. Pappas, Hamed Hassani

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adversarial Robustness with Semi-Infinite Constrained Learning.

Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimal Algorithms for Submodular Maximization with Distributed Constraints.

Alexander Robey, Arman Adibi, Brent Schlotfeldt, Hamed Hassani, George J. Pappas

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Learning Robust Hybrid Control Barrier Functions for Uncertain Systems.

Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni

Proceedings of the 7th IFAC Conference on Analysis and Design of Hybrid Systems, 2021

2020

Model-Based Robust Deep Learning.

Alexander Robey, Hamed Hassani, George J. Pappas

CoRR, 2020

Learning Hybrid Control Barrier Functions from Data.

Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni

Proceedings of the 4th Conference on Robot Learning, 2020

Learning Control Barrier Functions from Expert Demonstrations.

Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019

Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks.

Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, George J. Pappas

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019