Stephen Casper

According to our database, Stephen Casper authored at least 28 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience.

Zhonghao He, Jascha Achterberg, Katie Collins, Kevin K. Nejad, Danyal Akarca, Yinzhu Yang, Wes Gurnee, Ilia Sucholutsky, Yuhan Tang, Rebeca Ianov, George Ogden, Chole Li, Kai Sandbrink, Stephen Casper, Anna Ivanova, Grace W. Lindsay

CoRR, 2024

The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks From Artificial Intelligence.

Peter Slattery, Alexander K. Saeri, Emily A. C. Grundy, Jess Graham, Michael Noetel, Risto Uuk, James Dao, Soroush Pour, Stephen Casper, Neil Thompson

CoRR, 2024

Targeted Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs.

Abhay Sheshadri, Aidan Ewart, Phillip Guo, Aengus Lynch, Cindy Wu, Vivek Hebbar, Henry Sleight, Asa Cooper Stickland, Ethan Perez, Dylan Hadfield-Menell, Stephen Casper

CoRR, 2024

Open Problems in Technical AI Governance.

Anka Reuel, Ben Bucknall, Stephen Casper, Tim Fist, Lisa Soder, Onni Aarne, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, Markus Anderljung, Ben Garfinkel, Lennart Heim, Andrew Trask, Gabriel Mukobi, Rylan Schaeffer, Mauricio Baker, Sara Hooker, Irene Solaiman, Alexandra Sasha Luccioni, Nitarshan Rajkumar, Nicolas Moës, Jeffrey Ladish, Neel Guha, Jessica Newman, Yoshua Bengio, Tobin South, Alex Pentland, Sanmi Koyejo, Mykel J. Kochenderfer, Robert Trager

CoRR, 2024

Foundational Challenges in Assuring Alignment and Safety of Large Language Models.

Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger

CoRR, 2024

The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability.

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

CoRR, 2024

Defending Against Unforeseen Failure Modes with Latent Adversarial Training.

Stephen Casper, Lennart Schulze, Oam Patel, Dylan Hadfield-Menell

CoRR, 2024

Eight Methods to Evaluate Robust Unlearning in LLMs.

Aengus Lynch, Phillip Guo, Aidan Ewart, Stephen Casper, Dylan Hadfield-Menell

CoRR, 2024

Rethinking Machine Unlearning for Large Language Models.

Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Xiaojun Xu, Yuguang Yao, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu

CoRR, 2024

Black-Box Access is Insufficient for Rigorous AI Audits.

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

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

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

Trans. Mach. Learn. Res., 2023

Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation.

Rusheb Shah, Quentin Feuillade-Montixi, Soroush Pour, Arush Tagade, Stephen Casper, Javier Rando

CoRR, 2023

Measuring the Success of Diffusion Models at Imitating Human Artists.

Stephen Casper, Zifan Guo, Shreya Mogulothu, Zachary Marinov, Chinmay Deshpande, Rui-Jie Yew, Zheng Dai, Dylan Hadfield-Menell

CoRR, 2023

Explore, Establish, Exploit: Red Teaming Language Models from Scratch.

Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, Dylan Hadfield-Menell

CoRR, 2023

Benchmarking Interpretability Tools for Deep Neural Networks.

Stephen Casper, Yuxiao Li, Jiawei Li, Tong Bu, Kevin Zhang, Dylan Hadfield-Menell

CoRR, 2023

Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks.

Tilman Räuker, Anson Ho, Stephen Casper, Dylan Hadfield-Menell

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

Red Teaming Deep Neural Networks with Feature Synthesis Tools.

Stephen Casper, Tong Bu, Yuxiao Li, Jiawei Li, Kevin Zhang, Kaivalya Hariharan, Dylan Hadfield-Menell

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?

Kevin Liu, Stephen Casper, Dylan Hadfield-Menell, Jacob Andreas

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

White-Box Adversarial Policies in Deep Reinforcement Learning.

Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman

Proceedings of the Workshop on Artificial Intelligence Safety 2023 (SafeAI 2023) co-located with the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023), 2023

2022

Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks.

Stephen Casper, Kaivalya Hariharan, Dylan Hadfield-Menell

CoRR, 2022

Robust Feature-Level Adversaries are Interpretability Tools.

Stephen Casper, Max Nadeau, Dylan Hadfield-Menell, Gabriel Kreiman

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Detecting Modularity in Deep Neural Networks.

Shlomi Hod, Stephen Casper, Daniel Filan, Cody Wild, Andrew Critch, Stuart Russell

CoRR, 2021

One Thing to Fool them All: Generating Interpretable, Universal, and Physically-Realizable Adversarial Features.

Stephen Casper, Max Nadeau, Gabriel Kreiman

CoRR, 2021

Clusterability in Neural Networks.

Daniel Filan, Stephen Casper, Shlomi Hod, Cody Wild, Andrew Critch, Stuart Russell

CoRR, 2021

Frivolous Units: Wider Networks Are Not Really That Wide.

Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

The Achilles Heel Hypothesis: Pitfalls for AI Systems via Decision Theoretic Adversaries.

Stephen Casper

CoRR, 2020

Probing Neural Dialog Models for Conversational Understanding.

Abdelrhman Saleh, Tovly Deutsch, Stephen Casper, Yonatan Belinkov, Stuart M. Shieber

CoRR, 2020

2019

Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks.

Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman

CoRR, 2019