Artikel dengan mandat akses publik - Miao LiuPelajari lebih lanjut
Tersedia di suatu tempat: 9
Gaussian processes for learning and control: A tutorial with examples
M Liu, G Chowdhary, BC Da Silva, SY Liu, JP How
IEEE Control Systems Magazine 38 (5), 53-86, 2018
Mandat: US Department of Defense
Mitigating gradient bias in multi-objective learning: A provably convergent approach
H Fernando, H Shen, M Liu, S Chaudhury, K Murugesan, T Chen
International Conference on Learning Representations, 2023
Mandat: US National Science Foundation
Learning for decentralized control of multiagent systems in large, partially-observable stochastic environments
M Liu, C Amato, E Anesta, J Griffith, J How
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
Mandat: US National Science Foundation
Automatic pan–tilt camera control for learning dirichlet process gaussian process (dpgp) mixture models of multiple moving targets
H Wei, P Zhu, M Liu, JP How, S Ferrari
IEEE Transactions on Automatic Control 64 (1), 159-173, 2018
Mandat: US Department of Defense
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with -Greedy Exploration
S Zhang, H Li, M Wang, M Liu, PY Chen, S Lu, S Liu, K Murugesan, ...
Advances in Neural Information Processing Systems 36, 2024
Mandat: US National Science Foundation, US Department of Defense
IDYNO: Learning nonparametric DAGs from interventional dynamic data
T Gao, D Bhattacharjya, E Nelson, M Liu, Y Yu
International Conference on Machine Learning, 6988-7001, 2022
Mandat: US National Science Foundation, US Department of Defense
Information value in nonparametric Dirichlet-process Gaussian-process (DPGP) mixture models
H Wei, W Lu, P Zhu, S Ferrari, M Liu, RH Klein, S Omidshafiei, JP How
Automatica 74, 360-368, 2016
Mandat: US National Science Foundation
A Neuro-Symbolic Approach to Runtime Optimization in Resource Constrained Heterogeneous Systems
C Subramanian, S Swaminathan, M Liu, M Longinos, A Amarnath, ...
International Joint Conference on Artificial Intelligence, 2023
Mandat: US Department of Defense
Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments with Macro-actions
M Liu, C Amato, EP Anesta, JD Griffith, JP How
Mandat: US National Science Foundation
Informasi terbitan dan pendanaan ditentukan secara otomatis oleh program komputer