Causal Machine Learning: A Survey and Open Problems. - Jean Kaddour; Aengus Lynch; Qi Liu 049; Matt J. Kusner; Ricardo Silva 001 (CoRR, 2022)

Towards Automated Circuit Discovery for Mechanistic Interpretability. - Arthur Conmy; Augustine N. Mavor-Parker; Aengus Lynch; Stefan Heimersheim; Adrià Garriga-Alonso (NeurIPS, 2023)

Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases. - Aengus Lynch; Gbètondji J.-S. Dovonon; Jean Kaddour; Ricardo Silva 001 (CoRR, 2023)

Analysing the Generalisation and Reliability of Steering Vectors. - Daniel Tan; David Chanin; Aengus Lynch; Brooks Paige; Dimitrios Kanoulas; Adrià Garriga-Alonso; Robert Kirk (NeurIPS, 2024)

Eight Methods to Evaluate Robust Unlearning in LLMs. - Aengus Lynch; Phillip Guo; Aidan Ewart; Stephen Casper; Dylan Hadfield-Menell (CoRR, 2024)

Analyzing the Generalization and Reliability of Steering Vectors. - Daniel Tan; David Chanin; Aengus Lynch; Dimitrios Kanoulas; Brooks Paige; Adrià Garriga-Alonso; Robert Kirk (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)

Best-of-N Jailbreaking. - John Hughes; Sara Price; Aengus Lynch; Rylan Schaeffer; Fazl Barez; Sanmi Koyejo; Henry Sleight; Erik Jones; Ethan Perez; Mrinank Sharma (CoRR, 2024)

How Do Large Language Monkeys Get Their Power (Laws)? - Rylan Schaeffer; Joshua Kazdan; John Hughes; Jordan Juravsky; Sara Price; Aengus Lynch; Erik Jones; Robert Kirk; Azalia Mirhoseini; Sanmi Koyejo (CoRR, 2025)