| Neural networks with physics-informed architectures and constraints for dynamical systems modeling F Djeumou, C Neary, E Goubault, S Putot, U Topcu Learning for Dynamics and Control Conference, 263-277, 2022 | 82 | 2022 |
| Reward machines for cooperative multi-agent reinforcement learning C Neary, Z Xu, B Wu, U Topcu arXiv preprint arXiv:2007.01962, 2020 | 51 | 2020 |
| Verifiable and compositional reinforcement learning systems C Neary, C Verginis, M Cubuktepe, U Topcu Proceedings of the International Conference on Automated Planning and …, 2022 | 24 | 2022 |
| Taylor-lagrange neural ordinary differential equations: Toward fast training and evaluation of neural odes F Djeumou, C Neary, E Goubault, S Putot, U Topcu arXiv preprint arXiv:2201.05715, 2022 | 15 | 2022 |
| Compositional learning of dynamical system models using port-hamiltonian neural networks C Neary, U Topcu Learning for Dynamics and Control Conference, 679-691, 2023 | 9 | 2023 |
| Physics-informed kernel embeddings: Integrating prior system knowledge with data-driven control AJ Thorpe, C Neary, F Djeumou, MMK Oishi, U Topcu 2024 American Control Conference (ACC), 3130-3137, 2024 | 8 | 2024 |
| Planning not to talk: Multiagent systems that are robust to communication loss MO Karabag, C Neary, U Topcu arXiv preprint arXiv:2201.06619, 2022 | 8 | 2022 |
| Automaton-based representations of task knowledge from generative language models Y Yang, JR Gaglione, C Neary, U Topcu arXiv preprint arXiv:2212.01944, 2022 | 7 | 2022 |
| Differential privacy in cooperative multiagent planning B Chen, C Hawkins, MO Karabag, C Neary, M Hale, U Topcu Uncertainty in Artificial Intelligence, 347-357, 2023 | 6 | 2023 |
| Formal Methods for Autonomous Systems T Wongpiromsarn, M Ghasemi, M Cubuktepe, G Bakirtzis, S Carr, ... arXiv preprint arXiv:2311.01258, 2023 | 5 | 2023 |
| Smooth convex optimization using sub-zeroth-order oracles MO Karabag, C Neary, U Topcu Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 3815-3822, 2021 | 5 | 2021 |
| How to learn and generalize from three minutes of data: Physics-constrained and uncertainty-aware neural stochastic differential equations F Djeumou, C Neary, U Topcu arXiv preprint arXiv:2306.06335, 2023 | 4 | 2023 |
| Multiscale heterogeneous optimal lockdown control for COVID-19 using geographic information C Neary, M Cubuktepe, N Lauffer, X Jin, AJ Phillips, Z Xu, D Tong, ... Scientific reports 12 (1), 3970, 2022 | 2 | 2022 |
| A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning C Neary, C Ellis, AS Samyal, C Lennon, U Topcu 2024 IEEE International Conference on Robotics and Automation (ICRA), 4349-4355, 2024 | 1 | 2024 |
| Multimodal Pretrained Models for Sequential Decision-Making: Synthesis, Verification, Grounding, and Perception Y Yang, C Neary, U Topcu arXiv preprint arXiv:2308.05295, 2023 | 1 | 2023 |
| Large Language Models for Verifiable Sequential Decision-Making in Autonomous Systems Y Yang, JR Gaglione, C Neary 2nd Workshop on Language and Robot Learning: Language as Grounding, 0 | 1 | |
| Multimodal Pretrained Models for Verifiable Sequential Decision-Making: Planning, Grounding, and Perception Y Yang, C Neary NeurIPS 2023 Foundation Models for Decision Making Workshop, 2024 | | 2024 |
| Automatic Decomposition of Reward Machines for Decentralized Multiagent Reinforcement Learning S Smith, C Neary, U Topcu 2023 62nd IEEE Conference on Decision and Control (CDC), 5423-5430, 2023 | | 2023 |
| Verifiable Reinforcement Learning Systems via Compositionality C Neary, AS Samyal, C Verginis, M Cubuktepe, U Topcu arXiv preprint arXiv:2309.06420, 2023 | | 2023 |
| Control, Learning and Adaptation in Information-Constrained, Adversarial Environments YE Bayiz, S Carr, ES Crafts, M Cubuktepe, F Djeumou, M Ghasemi, ... | | 2023 |
Ufuk TopcuThe University of Texas at AustinVerified email at utexas.edu
