Rohin Shah
Rohin Shah
Graduate Student, Center for Human-Compatible AI
Verified email at - Homepage
Cited by
Cited by
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
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
Preferences Implicit in the State of the World
R Shah, D Krasheninnikov, J Alexander, P Abbeel, A Dragan
arXiv preprint arXiv:1902.04198, 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
The MAGICAL Benchmark for Robust Imitation
S Toyer, R Shah, A Critch, S Russell
Advances in Neural Information Processing Systems 33, 2020
Optimal Policies Tend to Seek Power
AM Turner, L Smith, R Shah, A Critch, P Tadepalli
arXiv preprint arXiv:1912.01683, 2019
Active Inverse Reward Design
S Mindermann, R Shah, A Gleave, D Hadfield-Menell
arXiv preprint arXiv:1809.03060, 2018
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
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
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
Benefits of Assistance over Reward Learning
R Shah, P Freire, N Alex, R Freedman, D Krasheninnikov, L Chan, ...
Choice Set Misspecification in Reward Inference
R Freedman, R Shah, A Dragan
CEUR Workshop Proceedings, 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
Automated Incrementalization through Synthesis
R Shah, R Bodik
Proceedings of the First Workshop on Incremental Computing, 2017
Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals
R Shah, V Varma, R Kumar, M Phuong, V Krakovna, J Uesato, Z Kenton
arXiv preprint arXiv:2210.01790, 2022
Learning What To Do by Simulating the Past
D Lindner, R Shah, P Abbeel, A Dragan
arXiv preprint arXiv:2104.03946, 2021
Retrospective on the 2021 MineRL BASALT Competition on Learning from Human Feedback
R Shah, SH Wang, C Wild, S Milani, A Kanervisto, VG Goecks, ...
NeurIPS 2021 Competitions and Demonstrations Track, 259-272, 2022
Combining reward information from multiple sources
D Krasheninnikov, R Shah, H van Hoof
arXiv preprint arXiv:2103.12142, 2021
SIMPL: A DSL for Automatic Specialization of Inference Algorithms
R Shah, E Torlak, R Bodik
arXiv preprint arXiv:1604.04729, 2016
SIRL: Similarity-based Implicit Representation Learning
A Bobu, Y Liu, R Shah, DS Brown, AD Dragan
arXiv preprint arXiv:2301.00810, 2023
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