Jacob Pfau

According to our database, Jacob Pfau authored at least 10 papers between 2019 and 2024.

Collaborative distances:

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

2024

Taking AI Welfare Seriously.

Robert Long, Jeff Sebo, Patrick Butlin, Kathleen Finlinson, Kyle Fish, Jacqueline Harding, Jacob Pfau, Toni Sims, Jonathan Birch, David Chalmers

CoRR, 2024

Steering Without Side Effects: Improving Post-Deployment Control of Language Models.

Asa Cooper Stickland, Alexander Lyzhov, Jacob Pfau, Salsabila Mahdi, Samuel R. Bowman

CoRR, 2024

Let's Think Dot by Dot: Hidden Computation in Transformer Language Models.

Jacob Pfau, William Merrill, Samuel R. Bowman

CoRR, 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

Self-Consistency of Large Language Models under Ambiguity.

Henning Bartsch, Ole Jorgensen, Domenic Rosati, Jason Hoelscher-Obermaier, Jacob Pfau

Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 2023

2022

Goal Misgeneralization in Deep Reinforcement Learning.

Lauro Langosco di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, David Krueger

Proceedings of the International Conference on Machine Learning, 2022

2021

Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models.

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

npj Digit. Medicine, 2021

Objective Robustness in Deep Reinforcement Learning.

Jack Koch, Lauro Langosco, Jacob Pfau, James Le, Lee Sharkey

CoRR, 2021

Robust Semantic Interpretability: Revisiting Concept Activation Vectors.

Jacob Pfau, Albert T. Young, Jerome Wei, Maria L. Wei, Michael J. Keiser

CoRR, 2021

2019

Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias.

Jacob Pfau, Albert T. Young, Maria L. Wei, Michael J. Keiser

CoRR, 2019