| Socially Aware Motion Planning with Deep Reinforcement Learning YF Chen, M Everett, M Liu, JP How arXiv preprint arXiv:1703.08862, 2017 | 826 | 2017 |
| Learning to learn without forgetting by maximizing transfer and minimizing interference M Riemer, I Cases, R Ajemian, M Liu, I Rish, Y Tu, G Tesauro arXiv preprint arXiv:1810.11910, 2018 | 806 | 2018 |
| Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning YF Chen, M Liu, M Everett, JP How arXiv preprint arXiv:1609.07845, 285 - 292, 2016 | 745 | 2016 |
| Eigenoption discovery through the deep successor representation MC Machado, C Rosenbaum, X Guo, M Liu, G Tesauro, M Campbell arXiv preprint arXiv:1710.11089, 2017 | 172 | 2017 |
| Learning to teach in cooperative multiagent reinforcement learning S Omidshafiei, DK Kim, M Liu, G Tesauro, M Riemer, C Amato, ... Proceedings of the AAAI conference on artificial intelligence 33 (01), 6128-6136, 2019 | 154 | 2019 |
| 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 | 114 | 2018 |
| Learning abstract options M Riemer, M Liu, G Tesauro Advances in neural information processing systems 31, 2018 | 93 | 2018 |
| A policy gradient algorithm for learning to learn in multiagent reinforcement learning DK Kim, M Liu, MD Riemer, C Sun, M Abdulhai, G Habibi, S Lopez-Cot, ... International Conference on Machine Learning, 5541-5550, 2021 | 63 | 2021 |
| Dynamic clustering via asymptotics of the dependent Dirichlet process mixture T Campbell, M Liu, B Kulis, JP How, L Carin Advances in Neural Information Processing Systems 26, 2013 | 63 | 2013 |
| Bi-parameter CGM model for approximation of a-stable PDF XT Li, J Sun, LW Jin, M Liu Electronics Letters 44 (18), 1096-1097, 2008 | 50 | 2008 |
| Off-policy reinforcement learning with Gaussian processes G Chowdhary, M Liu, R Grande, J How The 1st Multi-disciplinary Conference on Reinforcement Learning and Decision …, 2013 | 44 | 2013 |
| Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions M Liu, K Sivakumar, S Omidshafiei, C Amato, JP How arXiv preprint arXiv:1707.07399, 2017 | 42 | 2017 |
| 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 | 37 | 2016 |
| Learning hierarchical teaching policies for cooperative agents DK Kim, M Liu, S Omidshafiei, S Lopez-Cot, M Riemer, G Habibi, ... arXiv preprint arXiv:1903.03216, 2019 | 36 | 2019 |
| Motion Planning with Diffusion Maps Y Chen, S Liu, M Liu, J Miller, J How IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016 | 34 | 2016 |
| Augmented Dictionary Learning for Motion Prediction Y Chen, M Liu, J How The International Conference on Robotics and Automation, 2527 - 2534, 2016 | 33 | 2016 |
| Stick-breaking policy learning in Dec-POMDPs M Liu, C Amato, X Liao, L Carin, JP How International Joint Conference on Artificial Intelligence (IJCAI) 2015, 2015 | 33 | 2015 |
| 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 | 32 | 2023 |
| A zero-watermarking algorithm based on DWT and chaotic modulation H Cao, H Xiang, X Li, M Liu, S Yi, F Wei Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and …, 2006 | 32 | 2006 |
| Quickest change detection approach to optimal control in markov decision processes with model changes T Banerjee, M Liu, JP How 2017 American control conference (ACC), 399-405, 2017 | 29 | 2017 |
