3 citations · 4 across the 6 of their papers we have counts for
7 papers
Non-Linear Strategic Classification Made Practical
Jack Geary, Boyan Gao, Henry Gouk
Algorithmic developments in Strategic Classification have been mostly limited to linear classifiers in settings where the best response has a closed-form solution or can be easily…
The Signal in the Noise: OOD Detection Through Goodness-of-Fit Testing in Factorised Latent Spaces
Philipp Bomatter, Jack Geary, Henry Gouk
Deep generative models offer a natural foundation for out-of-distribution (OOD) detection, yet prior work has shown that their assigned likelihoods are notoriously unreliable indic…
Computing Strategic Responses to Non-Linear Classifiers
Jack Geary, Boyan Gao, Henry Gouk
We consider the problem of strategic classification, where the act of deploying a classifier leads to strategic behaviour that induces a distribution shift on subsequent observatio…
Strategic Classification with Randomised Classifiers
Jack Geary, Henry Gouk
We consider the problem of strategic classification, where a learner must build a model to classify agents based on features that have been strategically modified. Previous work in…
Active Altruism Learning and Information Sufficiency for Autonomous Driving
Jack Geary, Henry Gouk, Subramanian Ramamoorthy
Safe interaction between vehicles requires the ability to choose actions that reveal the preferences of the other vehicles. Since exploratory actions often do not directly contribu…
Resolving Conflict in Decision-Making for Autonomous Driving
Jack Geary, Subramanian Ramamoorthy, Henry Gouk
Recent work on decision making and planning for autonomous driving has made use of game theoretic methods to model interaction between agents. We demonstrate that methods based on…
Altruistic Decision-Making for Autonomous Driving with Sparse Rewards
Jack Geary, Henry Gouk
In order to drive effectively, a driver must be aware of how they can expect other vehicles' behaviour to be affected by their decisions, and also how they are expected to behave b…