3 papers
cs.LG2026
Mechanistic Anomaly Detection via Functional Attribution
Hugo Lyons Keenan, Christopher Leckie, Sarah Erfani
We can often verify the correctness of neural network outputs using ground truth labels, but we cannot reliably determine whether the output was produced by normal or anomalous int…
q-bio.QM2025
A microsimulation model of behaviour change calibrated to reversal learning data
Roben Delos Reyes, Hugo Lyons Keenan, Cameron Zachreson
Behaviour change lies at the heart of many observable collective phenomena such as the transmission and control of infectious diseases, adoption of public health policies, and migr…
cs.LG2025
HALO: Robust Out-of-Distribution Detection via Joint Optimisation
Hugo Lyons Keenan, Sarah Erfani, Christopher Leckie
Effective out-of-distribution (OOD) detection is crucial for the safe deployment of machine learning models in real-world scenarios. However, recent work has shown that OOD detecti…