Rohin Shah
Rohin Shah
Research Scientist, Google DeepMind
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
Optimal Policies Tend to Seek Power
AM Turner, L Smith, R Shah, A Critch, P Tadepalli
arXiv preprint arXiv:1912.01683, 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
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
The MAGICAL Benchmark for Robust Imitation
S Toyer, R Shah, A Critch, S Russell
Advances in Neural Information Processing Systems 33, 2020
Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla
T Lieberum, M Rahtz, J Kramár, G Irving, R Shah, V Mikulik
arXiv preprint arXiv:2307.09458, 2023
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
Active Inverse Reward Design
S Mindermann, R Shah, A Gleave, D Hadfield-Menell
arXiv preprint arXiv:1809.03060, 2018
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
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
Benefits of Assistance over Reward Learning
R Shah, P Freire, N Alex, R Freedman, D Krasheninnikov, L Chan, ...
Explaining grokking through circuit efficiency
V Varma, R Shah, Z Kenton, J Kramár, R Kumar
arXiv preprint arXiv:2309.02390, 2023
Choice Set Misspecification in Reward Inference
R Freedman, R Shah, A Dragan
CEUR Workshop Proceedings, 2020
SIRL: Similarity-based Implicit Representation Learning
A Bobu, Y Liu, R Shah, DS Brown, AD Dragan
Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot …, 2023
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
S Milani, A Kanervisto, K Ramanauskas, S Schulhoff, B Houghton, ...
arXiv preprint arXiv:2303.13512, 2023
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
Evaluating Frontier Models for Dangerous Capabilities
M Phuong, M Aitchison, E Catt, S Cogan, A Kaskasoli, V Krakovna, ...
arXiv preprint arXiv:2403.13793, 2024
Challenges with unsupervised LLM knowledge discovery
S Farquhar, V Varma, Z Kenton, J Gasteiger, V Mikulik, R Shah
arXiv preprint arXiv:2312.10029, 2023
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