Cyrus Neary

According to our database1, Cyrus Neary authored at least 25 papers between 2021 and 2026.

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Timeline

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Bibliography

2026
Value Vision-Language-Action Planning & Search.
CoRR, January, 2026

A Multi-Fidelity Control Variate Approach for Policy Gradient Estimation.
Trans. Mach. Learn. Res., 2026

2025
Designing policies for transition-independent multiagent systems that are robust to communication loss.
Auton. Agents Multi Agent Syst., December, 2025

ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning.
CoRR, October, 2025

Improving Pre-Trained Vision-Language-Action Policies with Model-Based Search.
CoRR, August, 2025

RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies.
CoRR, June, 2025

Multi-Fidelity Policy Gradient Algorithms.
CoRR, March, 2025

Automaton-Based Representations of Task Knowledge from Generative Language Models.
Proceedings of the International Conference on Neuro-symbolic Systems, 2025

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks.
Proceedings of the 64th IEEE Conference on Decision and Control, 2025

2024
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Multimodal Pretrained Models for Verifiable Sequential Decision-Making: Planning, Grounding, and Perception.
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.
Proceedings of the American Control Conference, 2024

2023
Formal Methods for Autonomous Systems.
Found. Trends Syst. Control., 2023

Verifiable Reinforcement Learning Systems via Compositionality.
CoRR, 2023

Multimodal Pretrained Models for Sequential Decision-Making: Synthesis, Verification, Grounding, and Perception.
CoRR, 2023

Differential Privacy in Cooperative Multiagent Planning.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks.
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.
Proceedings of the Conference on Robot Learning, 2023

Automatic Decomposition of Reward Machines for Decentralized Multiagent Reinforcement Learning.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling.
Proceedings of the Learning for Dynamics and Control Conference, 2022

Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss.
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

Verifiable and Compositional Reinforcement Learning Systems.
Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling, 2022

2021
Reward Machines for Cooperative Multi-Agent Reinforcement Learning.
Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

Smooth Convex Optimization Using Sub-Zeroth-Order Oracles.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021


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