Chlorophyll: Synthesis-aided compiler for low-power spatial architectures PM Phothilimthana, T Jelvis, R Shah, N Totla, S Chasins, R Bodik ACM SIGPLAN Notices 49 (6), 396-407, 2014 | 67 | 2014 |

On the utility of learning about humans for human-AI coordination M Carroll, R Shah, MK Ho, T Griffiths, S Seshia, P Abbeel, A Dragan Advances in Neural Information Processing Systems, 5174-5185, 2019 | 58 | 2019 |

Preferences Implicit in the State of the World R Shah, D Krasheninnikov, J Alexander, P Abbeel, A Dragan arXiv preprint arXiv:1902.04198, 2019 | 30 | 2019 |

On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference R Shah, N Gundotra, P Abbeel, A Dragan International Conference on Machine Learning, 5670-5679, 2019 | 20 | 2019 |

Active Inverse Reward Design S Mindermann, R Shah, A Gleave, D Hadfield-Menell arXiv preprint arXiv:1809.03060, 2018 | 15 | 2018 |

Optimal Policies Tend to Seek Power AM Turner, L Smith, R Shah, A Critch, P Tadepalli arXiv preprint arXiv:1912.01683, 2019 | 7* | 2019 |

The MAGICAL Benchmark for Robust Imitation S Toyer, R Shah, A Critch, S Russell Advances in Neural Information Processing Systems 33, 2020 | 6 | 2020 |

The implicit preference information in an initial state R Shah, D Krasheninnikov, J Alexander, P Abbeel, A Dragan International Conference on Learning Representations, 2019 | 5 | 2019 |

Benefits of Assistance over Reward Learning R Shah, P Freire, N Alex, R Freedman, D Krasheninnikov, L Chan, ... | 4 | 2020 |

Choice Set Misspecification in Reward Inference R Freedman, R Shah, A Dragan CEUR Workshop Proceedings, 2020 | 4 | 2020 |

Automated Incrementalization through Synthesis R Shah, R Bodik Proceedings of the First Workshop on Incremental Computing, 2017 | 3 | 2017 |

Evaluating the Robustness of Collaborative Agents P Knott, M Carroll, S Devlin, K Ciosek, K Hofmann, AD Dragan, R Shah arXiv preprint arXiv:2101.05507, 2021 | 2 | 2021 |

SIMPL: A DSL for Automatic Specialization of Inference Algorithms R Shah, E Torlak, R Bodik arXiv preprint arXiv:1604.04729, 2016 | 1 | 2016 |

An Empirical Investigation of Representation Learning for Imitation X Chen, S Toyer, C Wild, S Emmons, I Fischer, KH Lee, N Alex, SH Wang, ... Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | | 2021 |

The MineRL BASALT Competition on Learning from Human Feedback R Shah, C Wild, SH Wang, N Alex, B Houghton, W Guss, S Mohanty, ... arXiv preprint arXiv:2107.01969, 2021 | | 2021 |

Learning What To Do by Simulating the Past D Lindner, R Shah, P Abbeel, A Dragan arXiv preprint arXiv:2104.03946, 2021 | | 2021 |

Combining reward information from multiple sources D Krasheninnikov, R Shah, H van Hoof arXiv preprint arXiv:2103.12142, 2021 | | 2021 |

Extracting and Using Preference Information from the State of the World RM Shah University of California, Berkeley, 2020 | | 2020 |

Scalable Synthesis with Symbolic Syntax Graphs R Shah, S Kulal, R Bodik | | |

Program Synthesis for Forth R Bodik, MP Phothilimthana, T Jelvis, R Shah | | |