Cyrus Neary

According to our database, Cyrus Neary authored at least 17 papers between 2021 and 2024.

Collaborative distances:

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

2024

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks.

Cyrus Neary, Nathan Tsao, Ufuk Topcu

CoRR, 2024

A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning.

Cyrus Neary, Christian Ellis, Aryaman Singh Samyal, Craig Lennon, Ufuk Topcu

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Multimodal Pretrained Models for Verifiable Sequential Decision-Making: Planning, Grounding, and Perception.

Yunhao Yang, Cyrus Neary, Ufuk Topcu

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control.

Adam J. Thorpe, Cyrus Neary, Franck Djeumou, Meeko M. K. Oishi, Ufuk Topcu

Proceedings of the American Control Conference, 2024

2023

Formal Methods for Autonomous Systems.

Tichakorn Wongpiromsarn, Mahsa Ghasemi, Murat Cubuktepe, Georgios Bakirtzis, Steven Carr, Mustafa O. Karabag, Cyrus Neary, Parham Gohari, Ufuk Topcu

Found. Trends Syst. Control., 2023

Verifiable Reinforcement Learning Systems via Compositionality.

Cyrus Neary, Aryaman Singh Samyal, Christos K. Verginis, Murat Cubuktepe, Ufuk Topcu

CoRR, 2023

Multimodal Pretrained Models for Sequential Decision-Making: Synthesis, Verification, Grounding, and Perception.

Yunhao Yang, Cyrus Neary, Ufuk Topcu

CoRR, 2023

Differential Privacy in Cooperative Multiagent Planning.

Bo Chen, Calvin Hawkins, Mustafa O. Karabag, Cyrus Neary, Matthew T. Hale, Ufuk Topcu

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks.

Cyrus Neary, Ufuk Topcu

Proceedings of the Learning for Dynamics and Control Conference, 2023

How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations.

Franck Djeumou, Cyrus Neary, Ufuk Topcu

Proceedings of the Conference on Robot Learning, 2023

Automatic Decomposition of Reward Machines for Decentralized Multiagent Reinforcement Learning.

Sophia Smith, Cyrus Neary, Ufuk Topcu

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022

Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling.

Franck Djeumou, Cyrus Neary, Eric Goubault, Sylvie Putot, Ufuk Topcu

Proceedings of the Learning for Dynamics and Control Conference, 2022

Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs.

Franck Djeumou, Cyrus Neary, Eric Goubault, Sylvie Putot, Ufuk Topcu

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss.

Mustafa O. Karabag, Cyrus Neary, Ufuk Topcu

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

Verifiable and Compositional Reinforcement Learning Systems.

Cyrus Neary, Christos K. Verginis, Murat Cubuktepe, Ufuk Topcu

Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling, 2022

2021

Reward Machines for Cooperative Multi-Agent Reinforcement Learning.

Cyrus Neary, Zhe Xu, Bo Wu, Ufuk Topcu

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

Smooth Convex Optimization Using Sub-Zeroth-Order Oracles.

Mustafa O. Karabag, Cyrus Neary, Ufuk Topcu

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021