Ray: A distributed framework for emerging {AI} applications P Moritz, R Nishihara, S Wang, A Tumanov, R Liaw, E Liang, M Elibol, ... 13th {USENIX} Symposium on Operating Systems Design and Implementation …, 2018 | 820 | 2018 |

RLlib: Abstractions for distributed reinforcement learning E Liang, R Liaw, R Nishihara, P Moritz, R Fox, K Goldberg, J Gonzalez, ... International Conference on Machine Learning, 3053-3062, 2018 | 664* | 2018 |

Tune: A research platform for distributed model selection and training R Liaw, E Liang, R Nishihara, P Moritz, JE Gonzalez, I Stoica arXiv preprint arXiv:1807.05118, 2018 | 491 | 2018 |

Benchmarks for reinforcement learning in mixed-autonomy traffic E Vinitsky, A Kreidieh, L Le Flem, N Kheterpal, K Jang, C Wu, F Wu, ... Conference on robot learning, 399-409, 2018 | 114 | 2018 |

Real-time machine learning: The missing pieces R Nishihara, P Moritz, S Wang, A Tumanov, W Paul, J Schleier-Smith, ... Proceedings of the 16th Workshop on Hot Topics in Operating Systems, 106-110, 2017 | 62 | 2017 |

SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards S Krishnan, A Garg, R Liaw, B Thananjeyan, L Miller, FT Pokorny, ... The international journal of robotics research 38 (2-3), 126-145, 2019 | 46 | 2019 |

Large batch size training of neural networks with adversarial training and second-order information Z Yao, A Gholami, D Arfeen, R Liaw, J Gonzalez, K Keutzer, M Mahoney arXiv preprint arXiv:1810.01021, 2018 | 40 | 2018 |

Hirl: Hierarchical inverse reinforcement learning for long-horizon tasks with delayed rewards S Krishnan, A Garg, R Liaw, L Miller, FT Pokorny, K Goldberg arXiv preprint arXiv:1604.06508, 2016 | 34 | 2016 |

Hypersched: Dynamic resource reallocation for model development on a deadline R Liaw, R Bhardwaj, L Dunlap, Y Zou, JE Gonzalez, I Stoica, A Tumanov Proceedings of the ACM Symposium on Cloud Computing, 61-73, 2019 | 33 | 2019 |

Iterative noise injection for scalable imitation learning M Laskey, J Lee, W Hsieh, R Liaw, J Mahler, R Fox, K Goldberg arXiv preprint arXiv:1703.09327, 2017 | 22 | 2017 |

Composing meta-policies for autonomous driving using hierarchical deep reinforcement learning R Liaw, S Krishnan, A Garg, D Crankshaw, JE Gonzalez, K Goldberg arXiv preprint arXiv:1711.01503, 2017 | 18 | 2017 |

Ray: a distributed framework for emerging AI applications. CoRR abs/1712.05889 (2017) P Moritz, R Nishihara, S Wang, A Tumanov, R Liaw, E Liang, W Paul, ... arXiv preprint arXiv:1712.05889, 2017 | 18 | 2017 |

RubberBand: cloud-based hyperparameter tuning U Misra, R Liaw, L Dunlap, R Bhardwaj, K Kandasamy, JE Gonzalez, ... Proceedings of the Sixteenth European Conference on Computer Systems, 327-342, 2021 | 16 | 2021 |

SWIRL: A SequentialWindowed Inverse Reinforcement Learning Algorithm for Robot Tasks With Delayed Rewards S Krishnan, A Garg, R Liaw, B Thananjeyan, L Miller, FT Pokorny, ... Algorithmic Foundations of Robotics XII: Proceedings of the Twelfth Workshop …, 2020 | 13 | 2020 |

Impact: Importance weighted asynchronous architectures with clipped target networks M Luo, J Yao, R Liaw, E Liang, I Stoica arXiv preprint arXiv:1912.00167, 2019 | 5 | 2019 |

HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards. CoRR abs/1604.06508 (2016) S Krishnan, A Garg, R Liaw, L Miller, FT Pokorny, K Goldberg | 3 | 2016 |

Elastic Hyperparameter Tuning on the Cloud L Dunlap, K Kandasamy, U Misra, R Liaw, M Jordan, I Stoica, ... Proceedings of the ACM Symposium on Cloud Computing, 33-46, 2021 | 1 | 2021 |

ESCHER: expressive scheduling with ephemeral resources R Bhardwaj, A Tumanov, S Wang, R Liaw, P Moritz, R Nishihara, I Stoica Proceedings of the 13th Symposium on Cloud Computing, 47-62, 2022 | | 2022 |

REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale R Liaw, P Bailey, Y Li, M Dimakopoulou, Y Raimond Proceedings of the 16th ACM Conference on Recommender Systems, 684-685, 2022 | | 2022 |