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2020 – today
- 2023
[c3]Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman:
White-Box Adversarial Policies in Deep Reinforcement Learning. SafeAI@AAAI 2023- [i14]Stephen Casper, Yuxiao Li, Jiawei Li, Tong Bu, Kevin Zhang, Dylan Hadfield-Menell:
Benchmarking Interpretability Tools for Deep Neural Networks. CoRR abs/2302.10894 (2023) - [i13]Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, Dylan Hadfield-Menell:
Explore, Establish, Exploit: Red Teaming Language Models from Scratch. CoRR abs/2306.09442 (2023) - [i12]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 abs/2307.04028 (2023) - [i11]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 abs/2307.15217 (2023) - [i10]Rusheb Shah, Quentin Feuillade-Montixi, Soroush Pour, Arush Tagade, Stephen Casper, Javier Rando:
Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation. CoRR abs/2311.03348 (2023) - 2022
- [c2]Stephen Casper, Max Nadeau, Dylan Hadfield-Menell, Gabriel Kreiman:
Robust Feature-Level Adversaries are Interpretability Tools. NeurIPS 2022 - [i9]Tilman Räuker, Anson Ho, Stephen Casper, Dylan Hadfield-Menell:
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. CoRR abs/2207.13243 (2022) - [i8]Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman:
White-Box Adversarial Policies in Deep Reinforcement Learning. CoRR abs/2209.02167 (2022) - [i7]Stephen Casper, Kaivalya Hariharan, Dylan Hadfield-Menell:
Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks. CoRR abs/2211.10024 (2022) - 2021
- [c1]Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman:
Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021: 6921-6929 - [i6]Daniel Filan, Stephen Casper, Shlomi Hod, Cody Wild, Andrew Critch, Stuart Russell:
Clusterability in Neural Networks. CoRR abs/2103.03386 (2021) - [i5]Stephen Casper, Max Nadeau, Gabriel Kreiman:
One Thing to Fool them All: Generating Interpretable, Universal, and Physically-Realizable Adversarial Features. CoRR abs/2110.03605 (2021) - [i4]Shlomi Hod, Stephen Casper, Daniel Filan, Cody Wild, Andrew Critch, Stuart Russell:
Detecting Modularity in Deep Neural Networks. CoRR abs/2110.08058 (2021) - 2020
- [i3]Abdelrhman Saleh, Tovly Deutsch, Stephen Casper, Yonatan Belinkov, Stuart M. Shieber:
Probing Neural Dialog Models for Conversational Understanding. CoRR abs/2006.08331 (2020) - [i2]Stephen Casper:
The Achilles Heel Hypothesis: Pitfalls for AI Systems via Decision Theoretic Adversaries. CoRR abs/2010.05418 (2020)
2010 – 2019
- 2019
- [i1]Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf
, Kasper Vinken, Gabriel Kreiman:
Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks. CoRR abs/1912.04783 (2019)
Coauthor Index
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