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2020 – today
- 2024
[i8]Jacob Pfau, William Merrill, Samuel R. Bowman:
Let's Think Dot by Dot: Hidden Computation in Transformer Language Models. CoRR abs/2404.15758 (2024)- [i7]Asa Cooper Stickland, Alexander Lyzhov, Jacob Pfau, Salsabila Mahdi, Samuel R. Bowman:
Steering Without Side Effects: Improving Post-Deployment Control of Language Models. CoRR abs/2406.15518 (2024) - [i6]Robert Long, Jeff Sebo, Patrick Butlin, Kathleen Finlinson, Kyle Fish, Jacqueline Harding, Jacob Pfau, Toni Sims, Jonathan Birch, David Chalmers:
Taking AI Welfare Seriously. CoRR abs/2411.00986 (2024) - 2023
- [j2]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) - [c2]Henning Bartsch, Ole Jorgensen, Domenic Rosati, Jason Hoelscher-Obermaier, Jacob Pfau:
Self-Consistency of Large Language Models under Ambiguity. BlackboxNLP@EMNLP 2023: 89-105 - [i5]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. CoRR abs/2307.15217 (2023) - [i4]Henning Bartsch, Ole Jorgensen, Domenic Rosati, Jason Hoelscher-Obermaier
, Jacob Pfau:
Self-Consistency of Large Language Models under Ambiguity. CoRR abs/2310.13439 (2023) - 2022
- [c1]Lauro Langosco di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, David Krueger:
Goal Misgeneralization in Deep Reinforcement Learning. ICML 2022: 12004-12019 - 2021
- [j1]Albert T. Young, Kristen Fernandez, Jacob Pfau, Rasika Reddy, Nhat Anh Cao, Max Y. von Franque, Arjun Johal, Benjamin V. Wu, Rachel R. Wu, Jennifer Y. Chen, Raj P. Fadadu, Juan A. Vasquez, Andrew Tam, Michael J. Keiser, Maria L. Wei:
Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models. npj Digit. Medicine 4 (2021) - [i3]Jacob Pfau, Albert T. Young, Jerome Wei, Maria L. Wei, Michael J. Keiser:
Robust Semantic Interpretability: Revisiting Concept Activation Vectors. CoRR abs/2104.02768 (2021) - [i2]Jack Koch, Lauro Langosco, Jacob Pfau, James Le, Lee Sharkey:
Objective Robustness in Deep Reinforcement Learning. CoRR abs/2105.14111 (2021)
2010 – 2019
- 2019
- [i1]Jacob Pfau, Albert T. Young, Maria L. Wei, Michael J. Keiser:
Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias. CoRR abs/1910.07604 (2019)
Coauthor Index
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