Chlorophyll: synthesis-aided compiler for low-power spatial architectures. - Phitchaya Mangpo Phothilimthana; Tikhon Jelvis; Rohin Shah; Nishant Totla; Sarah E. Chasins; Rastislav Bodík (PLDI, 2014)

SIMPL: A DSL for Automatic Specialization of Inference Algorithms. - Rohin Shah; Emina Torlak; Rastislav Bodík (CoRR, 2016)

Active Inverse Reward Design. - Sören Mindermann; Rohin Shah; Adam Gleave; Dylan Hadfield-Menell (CoRR, 2018)

Preferences Implicit in the State of the World. - Rohin Shah; Dmitrii Krasheninnikov; Jordan Alexander; Pieter Abbeel; Anca D. Dragan (ICLR (Poster), 2019)

On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference. - Rohin Shah; Noah Gundotra; Pieter Abbeel; Anca D. Dragan (ICML, 2019)

On the Utility of Learning about Humans for Human-AI Coordination. - Micah Carroll; Rohin Shah; Mark K. Ho; Tom Griffiths 001; Sanjit A. Seshia; Pieter Abbeel; Anca D. Dragan (NeurIPS, 2019)

Choice Set Misspecification in Reward Inference. - Rachel Freedman; Rohin Shah; Anca D. Dragan (AISafety@IJCAI, 2020)

The MAGICAL Benchmark for Robust Imitation. - Sam Toyer; Rohin Shah; Andrew Critch; Stuart Russell 001 (NeurIPS, 2020)

Extracting and Using Preference Information from the State of the World. - Rohin Shah (2020)

Evaluating the Robustness of Collaborative Agents. - Paul A. Knott; Micah Carroll; Sam Devlin; Kamil Ciosek; Katja Hofmann; Anca D. Dragan; Rohin Shah (AAMAS, 2021)

Learning What To Do by Simulating the Past. - David Lindner; Rohin Shah; Pieter Abbeel; Anca D. Dragan (ICLR, 2021)

An Empirical Investigation of Representation Learning for Imitation. - Cynthia Chen; Xin Chen; Sam Toyer; Cody Wild; Scott Emmons; Ian Fischer; Kuang-Huei Lee; Neel Alex; Steven H. Wang; Ping Luo; Stuart Russell 001; Pieter Abbeel; et al. (NeurIPS Datasets and Benchmarks, 2021)

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition. - Stephanie Milani; Anssi Kanervisto; Karolis Ramanauskas; Sander Schulhoff; Brandon Houghton; Sharada P. Mohanty; Byron Galbraith; Ke Chen; Yan Song; Tianze Zhou; Bingquan Yu; He Liu; et al. (NeurIPS (Competition and Demos), 2021)

Retrospective on the 2021 MineRL BASALT Competition on Learning from Human Feedback. - Rohin Shah; Steven H. Wang; Cody Wild; Stephanie Milani; Anssi Kanervisto; Vinicius G. Goecks; Nicholas R. Waytowich; David Watkins-Valls; Bharat Prakash; Edmund Mills; Divyansh Garg; Alexander Fries; et al. (NeurIPS (Competition and Demos), 2021)

Optimal Policies Tend To Seek Power. - Alexander Matt Turner; Logan Smith; Rohin Shah; Andrew Critch; Prasad Tadepalli (NeurIPS, 2021)

Combining Reward Information from Multiple Sources. - Dmitrii Krasheninnikov; Rohin Shah; Herke van Hoof (CoRR, 2021)

The MineRL BASALT Competition on Learning from Human Feedback. - Rohin Shah; Cody Wild; Steven H. Wang; Neel Alex; Brandon Houghton; William H. Guss; Sharada P. Mohanty; Anssi Kanervisto; Stephanie Milani; Nicholay Topin; Pieter Abbeel; Stuart Russell 001; et al. (CoRR, 2021)

Retrospective on the 2021 BASALT Competition on Learning from Human Feedback. - Rohin Shah; Steven H. Wang; Cody Wild; Stephanie Milani; Anssi Kanervisto; Vinicius G. Goecks; Nicholas R. Waytowich; David Watkins-Valls; Bharat Prakash; Edmund Mills; Divyansh Garg; Alexander Fries; et al. (CoRR, 2022)

An Empirical Investigation of Representation Learning for Imitation. - Xin Chen; Sam Toyer; Cody Wild; Scott Emmons; Ian Fischer; Kuang-Huei Lee; Neel Alex; Steven H. Wang; Ping Luo; Stuart Russell 001; Pieter Abbeel; Rohin Shah (CoRR, 2022)

Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals. - Rohin Shah; Vikrant Varma; Ramana Kumar; Mary Phuong; Victoria Krakovna; Jonathan Uesato; Zac Kenton (CoRR, 2022)

SIRL: Similarity-based Implicit Representation Learning. - Andreea Bobu; Yi Liu; Rohin Shah; Daniel S. Brown; Anca D. Dragan (HRI, 2023)

BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks. - Stephanie Milani; Anssi Kanervisto; Karolis Ramanauskas; Sander Schulhoff; Brandon Houghton; Rohin Shah (NeurIPS, 2023)

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition. - Stephanie Milani; Anssi Kanervisto; Karolis Ramanauskas; Sander Schulhoff; Brandon Houghton; Sharada P. Mohanty; Byron Galbraith; Ke Chen; Yan Song; Tianze Zhou; Bingquan Yu; He Liu; et al. (CoRR, 2023)

Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla. - Tom Lieberum; Matthew Rahtz; János Kramár; Neel Nanda; Geoffrey Irving; Rohin Shah; Vladimir Mikulik (CoRR, 2023)

Explaining grokking through circuit efficiency. - Vikrant Varma; Rohin Shah; Zachary Kenton; János Kramár; Ramana Kumar (CoRR, 2023)

Challenges with unsupervised LLM knowledge discovery. - Sebastian Farquhar; Vikrant Varma; Zachary Kenton; Johannes Gasteiger; Vladimir Mikulik; Rohin Shah (CoRR, 2023)

On scalable oversight with weak LLMs judging strong LLMs. - Zachary Kenton; Noah Y. Siegel; János Kramár; Jonah Brown-Cohen; Samuel Albanie; Jannis Bulian; Rishabh Agarwal; David Lindner; Yunhao Tang; Noah D. Goodman; Rohin Shah (NeurIPS, 2024)

Improving Sparse Decomposition of Language Model Activations with Gated Sparse Autoencoders. - Senthooran Rajamanoharan; Arthur Conmy; Lewis Smith; Tom Lieberum; Vikrant Varma; János Kramár; Rohin Shah; Neel Nanda (NeurIPS, 2024)

AtP*: An efficient and scalable method for localizing LLM behaviour to components. - János Kramár; Tom Lieberum; Rohin Shah; Neel Nanda (CoRR, 2024)

Evaluating Frontier Models for Dangerous Capabilities. - Mary Phuong; Matthew Aitchison; Elliot Catt; Sarah Cogan; Alexandre Kaskasoli; Victoria Krakovna; David Lindner; Matthew Rahtz; Yannis Assael; Sarah Hodkinson; Heidi Howard; Tom Lieberum; et al. (CoRR, 2024)

Improving Dictionary Learning with Gated Sparse Autoencoders. - Senthooran Rajamanoharan; Arthur Conmy; Lewis Smith; Tom Lieberum; Vikrant Varma; János Kramár; Rohin Shah; Neel Nanda (CoRR, 2024)

Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2. - Tom Lieberum; Senthooran Rajamanoharan; Arthur Conmy; Lewis Smith; Nicolas Sonnerat; Vikrant Varma; János Kramár; Anca D. Dragan; Rohin Shah; Neel Nanda (CoRR, 2024)

MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking. - Sebastian Farquhar; Vikrant Varma; David Lindner; David K. Elson; Caleb Biddulph; Ian Goodfellow; Rohin Shah (CoRR, 2025)

An Approach to Technical AGI Safety and Security. - Rohin Shah; Alex Irpan; Alexander Matt Turner; Anna Wang; Arthur Conmy; David Lindner; Jonah Brown-Cohen; Lewis Ho; Neel Nanda; Raluca Ada Popa; Rishub Jain; Rory Greig; et al. (CoRR, 2025)

Evaluating the Goal-Directedness of Large Language Models. - Tom Everitt; Cristina Garbacea; Alexis Bellot; Jonathan Richens; Henry Papadatos; Siméon Campos; Rohin Shah (CoRR, 2025)

Evaluating Frontier Models for Stealth and Situational Awareness. - Mary Phuong; Roland S. Zimmermann; Ziyue Wang; David Lindner; Victoria Krakovna; Sarah Cogan; Allan Dafoe; Lewis Ho; Rohin Shah (CoRR, 2025)

When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors. - Scott Emmons; Erik Jenner; David K. Elson; Rif A. Saurous; Senthooran Rajamanoharan; Heng Chen; Irhum Shafkat; Rohin Shah (CoRR, 2025)

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety. - Tomek Korbak; Mikita Balesni; Elizabeth Barnes; Yoshua Bengio; Joe Benton; Joseph Bloom; Mark Chen 003; Alan Cooney; Allan Dafoe; Anca D. Dragan; Scott Emmons; Owain Evans; et al. (CoRR, 2025)