Lee Sharkey

According to our database, Lee Sharkey authored at least 10 papers between 2021 and 2024.

Collaborative distances:

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

2024

Interpretability as Compression: Reconsidering SAE Explanations of Neural Activations with MDL-SAEs.

Kola Ayonrinde, Michael T. Pearce, Lee Sharkey

CoRR, 2024

Bilinear MLPs enable weight-based mechanistic interpretability.

Michael T. Pearce, Thomas Dooms, Alice Rigg, José Oramas M., Lee Sharkey

CoRR, 2024

Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning.

Dan Braun, Jordan Taylor, Nicholas Goldowsky-Dill, Lee Sharkey

CoRR, 2024

Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Robert Huben, Hoagy Cunningham, Logan Riggs, Aidan Ewart, Lee Sharkey

Proceedings of the Twelfth International Conference on Learning Representations, 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

A technical note on bilinear layers for interpretability.

Lee Sharkey

CoRR, 2023

2022

Circumventing interpretability: How to defeat mind-readers.

Lee Sharkey

CoRR, 2022

Interpreting Neural Networks through the Polytope Lens.

Sid Black, Lee Sharkey, Léo Grinsztajn, Eric Winsor, Dan Braun, Jacob Merizian, Kip Parker, Carlos Ramón Guevara, Beren Millidge, Gabriel Alfour, Connor Leahy

CoRR, 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

Objective Robustness in Deep Reinforcement Learning.

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

CoRR, 2021