Dylan Hadfield-Menell

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 84

Top Topics

Optimal Planning

Reinforcement Learning

Diffusion Models

Top Venues

Publications

Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas A. Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell
Black-Box Access is Insufficient for Rigorous AI Audits. FAccT (2024)
Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Alexander Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell
Black-Box Access is Insufficient for Rigorous AI Audits. CoRR (2024)
Jonathan Stray, Alon Y. Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Chloé Bakalar, Lex Beattie, Michael D. Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Xian Zhang, McKane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. Trans. Recomm. Syst. 2 (3) (2024)
Stephen Casper, Lennart Schulze, Oam Patel, Dylan Hadfield-Menell
Defending Against Unforeseen Failure Modes with Latent Adversarial Training. CoRR (2024)
Abhay Sheshadri, Aidan Ewart, Phillip Guo, Aengus Lynch, Cindy Wu, Vivek Hebbar, Henry Sleight, Asa Cooper Stickland, Ethan Perez, Dylan Hadfield-Menell, Stephen Casper
Targeted Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs. CoRR (2024)
Aengus Lynch, Phillip Guo, Aidan Ewart, Stephen Casper, Dylan Hadfield-Menell
Eight Methods to Evaluate Robust Unlearning in LLMs. CoRR (2024)
Stephen Casper, Jieun Yun, Joonhyuk Baek, Yeseong Jung, Minhwan Kim, Kiwan Kwon, Saerom Park, Hayden Moore, David Shriver, Marissa Connor, Keltin Grimes, Angus Nicolson, Arush Tagade, Jessica Rumbelow, Hieu Minh Nguyen, Dylan Hadfield-Menell
The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability. CoRR (2024)
Anand Siththaranjan, Cassidy Laidlaw, Dylan Hadfield-Menell
Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF. ICLR (2024)
Anand Siththaranjan, Cassidy Laidlaw, Dylan Hadfield-Menell
Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF. CoRR (2023)
Stephen Casper, Yuxiao Li, Jiawei Li, Tong Bu, Kevin Zhang, Dylan Hadfield-Menell
Benchmarking Interpretability Tools for Deep Neural Networks. CoRR (2023)
Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, Dylan Hadfield-Menell
Explore, Establish, Exploit: Red Teaming Language Models from Scratch. CoRR (2023)
Stephen Casper, Zifan Guo, Shreya Mogulothu, Zachary Marinov, Chinmay Deshpande, Rui-Jie Yew, Zheng Dai, Dylan Hadfield-Menell
Measuring the Success of Diffusion Models at Imitating Human Artists. CoRR (2023)
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Tong Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. Trans. Mach. Learn. Res. 2023 (2023)
Kevin Liu, Stephen Casper, Dylan Hadfield-Menell, Jacob Andreas
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness? CoRR (2023)
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. CoRR (2023)
Stephen Casper, Tong Bu, Yuxiao Li, Jiawei Li, Kevin Zhang, Kaivalya Hariharan, Dylan Hadfield-Menell
Red Teaming Deep Neural Networks with Feature Synthesis Tools. NeurIPS (2023)
Tilman Räuker, Anson Ho, Stephen Casper, Dylan Hadfield-Menell
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. SaTML (2023)
Andreas A. Haupt, Dylan Hadfield-Menell, Chara Podimata
Recommending to Strategic Users. CoRR (2023)
Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman
White-Box Adversarial Policies in Deep Reinforcement Learning. SafeAI@AAAI (2023)
Kevin Liu, Stephen Casper, Dylan Hadfield-Menell, Jacob Andreas
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness? EMNLP (2023)
Phillip J. K. Christoffersen, Andreas A. Haupt, Dylan Hadfield-Menell
Get It in Writing: Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL. AAMAS (2023)
Stephen Casper, Kaivalya Hariharan, Dylan Hadfield-Menell
Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks. CoRR (2022)
Micah D. Carroll, Anca D. Dragan, Stuart Russell, Dylan Hadfield-Menell
Estimating and Penalizing Induced Preference Shifts in Recommender Systems. ICML (2022)
Tilman Räuker, Anson Ho, Stephen Casper, Dylan Hadfield-Menell
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. CoRR (2022)
Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman
White-Box Adversarial Policies in Deep Reinforcement Learning. CoRR (2022)
Mihaela Curmei, Andreas A. Haupt, Dylan Hadfield-Menell, Benjamin Recht
Towards Psychologically-Grounded Dynamic Preference Models. CoRR (2022)
Micah Carroll, Dylan Hadfield-Menell, Stuart Russell, Anca D. Dragan
Estimating and Penalizing Induced Preference Shifts in Recommender Systems. CoRR (2022)
Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell
Linguistic communication as (inverse) reward design. CoRR (2022)
Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell
How to talk so your robot will learn: Instructions, descriptions, and pragmatics. CoRR (2022)
Rui-Jie Yew, Dylan Hadfield-Menell
A Penalty Default Approach to Preemptive Harm Disclosure and Mitigation for AI Systems. AIES (2022)
Stephen Casper, Max Nadeau, Dylan Hadfield-Menell, Gabriel Kreiman
Robust Feature-Level Adversaries are Interpretability Tools. NeurIPS (2022)
Mihaela Curmei, Andreas A. Haupt, Benjamin Recht, Dylan Hadfield-Menell
Towards Psychologically-Grounded Dynamic Preference Models. RecSys (2022)
Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Tom Griffiths, Dylan Hadfield-Menell
How to talk so AI will learn: Instructions, descriptions, and autonomy. NeurIPS (2022)
Phillip J. K. Christoffersen, Andreas A. Haupt, Dylan Hadfield-Menell
Get It in Writing: Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL. CoRR (2022)
Jonathan Stray, Alon Y. Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Lex Beattie, Michael D. Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy X. Zhang, McKane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. CoRR (2022)
Liu Leqi, Dylan Hadfield-Menell, Zachary C. Lipton
When Curation Becomes Creation: Algorithms, Microcontent, and the Vanishing Distinction between Platforms and Creators. CoRR (2021)
Simon Zhuang, Dylan Hadfield-Menell
Consequences of Misaligned AI. CoRR (2021)
Liu Leqi, Dylan Hadfield-Menell, Zachary C. Lipton
When curation becomes creation. Commun. ACM 64 (12) (2021)
Jonathan Stray, Ivan Vendrov, Jeremy Nixon, Steven Adler, Dylan Hadfield-Menell
What are you optimizing for? Aligning Recommender Systems with Human Values. CoRR (2021)
Liu Leqi, Dylan Hadfield-Menell, Zachary C. Lipton
When Curation Becomes Creation: Algorithms, microcontent, and the vanishing distinction between platforms and creators. ACM Queue 19 (3) (2021)
Micah Carroll, Dylan Hadfield-Menell, Stuart Russell, Anca D. Dragan
Estimating and Penalizing Preference Shift in Recommender Systems. RecSys (2021)
Michael James McDonald, Dylan Hadfield-Menell
Guided Imitation of Task and Motion Planning. CoRR (2021)
Michael James McDonald, Dylan Hadfield-Menell
Guided Imitation of Task and Motion Planning. CoRL (2021)
Raphael Köster, Dylan Hadfield-Menell, Gillian K. Hadfield, Joel Z. Leibo
Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors. CoRR (2020)
Arnaud Fickinger, Simon Zhuang, Andrew Critch, Dylan Hadfield-Menell, Stuart Russell
Multi-Principal Assistance Games: Definition and Collegial Mechanisms. CoRR (2020)
Alexander Matt Turner, Dylan Hadfield-Menell, Prasad Tadepalli
Conservative Agency via Attainable Utility Preservation. AIES (2020)
Simon Zhuang, Dylan Hadfield-Menell
Consequences of Misaligned AI. NeurIPS (2020)
Arnaud Fickinger, Simon Zhuang, Dylan Hadfield-Menell, Stuart J. Russell
Multi-Principal Assistance Games. CoRR (2020)
Raphael Koster, Dylan Hadfield-Menell, Gillian K. Hadfield, Joel Z. Leibo
Silly Rules Improve the Capacity of Agents to Learn Stable Enforcement and Compliance Behaviors. AAMAS (2020)
Dylan Hadfield-Menell, McKane Andrus, Gillian K. Hadfield
Legible Normativity for AI Alignment: The Value of Silly Rules. AIES (2019)
Lawrence Chan, Dylan Hadfield-Menell, Siddhartha S. Srinivasa, Anca D. Dragan
The Assistive Multi-Armed Bandit. CoRR (2019)
Alexander Matt Turner, Dylan Hadfield-Menell, Prasad Tadepalli
Conservative Agency via Attainable Utility Preservation. CoRR (2019)
Rohan Choudhury, Gokul Swamy, Dylan Hadfield-Menell, Anca D. Dragan
On the Utility of Model Learning in HRI. HRI (2019)
Ravi Pandya, Sandy H. Huang, Dylan Hadfield-Menell, Anca D. Dragan
Human-AI Learning Performance in Multi-Armed Bandits. AIES (2019)
Marc Khoury, Dylan Hadfield-Menell
Adversarial Training with Voronoi Constraints. CoRR (2019)
Lawrence Chan, Dylan Hadfield-Menell, Siddhartha S. Srinivasa, Anca D. Dragan
The Assistive Multi-Armed Bandit. HRI (2019)
Matthew Rahtz, James Fang, Anca D. Dragan, Dylan Hadfield-Menell
An Extensible Interactive Interface for Agent Design. CoRR (2019)
Rohan Choudhury, Gokul Swamy, Dylan Hadfield-Menell, Anca D. Dragan
On the Utility of Model Learning in HRI. CoRR (2019)
Alexander Matt Turner, Dylan Hadfield-Menell, Prasad Tadepalli
Conservative Agency. AISafety@IJCAI (2019)
Dylan Hadfield-Menell, Gillian K. Hadfield
Incomplete Contracting and AI Alignment. AIES (2019)
Dylan Hadfield-Menell, Gillian K. Hadfield
Incomplete Contracting and AI Alignment. CoRR (2018)
Dhruv Malik, Malayandi Palaniappan, Jaime F. Fisac, Dylan Hadfield-Menell, Stuart J. Russell, Anca D. Dragan
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning. CoRR (2018)
Allan Zhou, Dylan Hadfield-Menell, Anusha Nagabandi, Anca D. Dragan
Expressive Robot Motion Timing. CoRR (2018)
Ellis Ratner, Dylan Hadfield-Menell, Anca D. Dragan
Simplifying Reward Design through Divide-and-Conquer. Robotics: Science and Systems (2018)
Ravi Pandya, Sandy H. Huang, Dylan Hadfield-Menell, Anca D. Dragan
Human-AI Learning Performance in Multi-Armed Bandits. CoRR (2018)
Dylan Hadfield-Menell, McKane Andrus, Gillian K. Hadfield
Legible Normativity for AI Alignment: The Value of Silly Rules. CoRR (2018)
Dhruv Malik, Malayandi Palaniappan, Jaime F. Fisac, Dylan Hadfield-Menell, Stuart J. Russell, Anca D. Dragan
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning. ICML (2018)
Ellis Ratner, Dylan Hadfield-Menell, Anca D. Dragan
Simplifying Reward Design through Divide-and-Conquer. CoRR (2018)
Marc Khoury, Dylan Hadfield-Menell
On the Geometry of Adversarial Examples. CoRR (2018)
Sören Mindermann, Rohin Shah, Adam Gleave, Dylan Hadfield-Menell
Active Inverse Reward Design. CoRR (2018)
Allan Zhou, Dylan Hadfield-Menell, Anusha Nagabandi, Anca D. Dragan
Expressive Robot Motion Timing. HRI (2017)
Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan
Pragmatic-Pedagogic Value Alignment. ISRR (2017)
Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan
Pragmatic-Pedagogic Value Alignment. CoRR (2017)
Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart J. Russell
The Off-Switch Game. IJCAI (2017)
Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J. Russell, Anca D. Dragan
Inverse Reward Design. CoRR (2017)
Smitha Milli, Dylan Hadfield-Menell, Anca D. Dragan, Stuart J. Russell
Should Robots be Obedient? CoRR (2017)
Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart J. Russell
The Off-Switch Game. AAAI Workshops (2017)
Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J. Russell, Anca D. Dragan
Inverse Reward Design. NIPS (2017)
Smitha Milli, Dylan Hadfield-Menell, Anca D. Dragan, Stuart J. Russell
Should Robots be Obedient? IJCAI (2017)
Dylan Hadfield-Menell, Christopher Lin, Rohan Chitnis, Stuart J. Russell, Pieter Abbeel
Sequential quadratic programming for task plan optimization. IROS (2016)
Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart J. Russell
Cooperative Inverse Reinforcement Learning. CoRR (2016)
Rohan Chitnis, Dylan Hadfield-Menell, Abhishek Gupta, Siddharth Srivastava, Edward Groshev, Christopher Lin, Pieter Abbeel
Guided search for task and motion plans using learned heuristics. ICRA (2016)
Dylan Hadfield-Menell, Stuart J. Russell, Pieter Abbeel, Anca D. Dragan
Cooperative Inverse Reinforcement Learning. NIPS (2016)
Dylan Hadfield-Menell, Anca D. Dragan, Pieter Abbeel, Stuart J. Russell
The Off-Switch Game. CoRR (2016)
Dylan Hadfield-Menell, Alex X. Lee, Chelsea Finn, Eric Tzeng, Sandy H. Huang, Pieter Abbeel
Beyond lowest-warping cost action selection in trajectory transfer. ICRA (2015)
Dylan Hadfield-Menell, Edward Groshev, Rohan Chitnis, Pieter Abbeel
Modular task and motion planning in belief space. IROS (2015)
Dylan Hadfield-Menell, Stuart J. Russell
Multitasking: Optimal Planning for Bandit Superprocesses. UAI (2015)
Alex X. Lee, Sandy H. Huang, Dylan Hadfield-Menell, Eric Tzeng, Pieter Abbeel
Unifying scene registration and trajectory optimization for learning from demonstrations with application to manipulation of deformable objects. IROS (2014)
Dylan Hadfield-Menell, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Optimization in the now: Dynamic peephole optimization for hierarchical planning. ICRA (2013)