9 citations · 24 across the 13 of their papers we have counts for
14 papers
Safety from Honesty in a Disinterested AI Predictor
Yoshua Bengio, Oliver Richardson, Tomáš Gavenčiak +13
As AI systems become more capable, training procedures that optimize for downstream outcomes risk introducing implicit agency: goal-directed behavior that designers never specified…
Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithm
Yang Chen, Menglin Zou, Jiaqi Zhang +6
Inverse Reinforcement Learning (IRL) learns a reward function to explain expert demonstrations. Modern IRL methods often use the adversarial (minimax) formulation that alternates b…
Causal Cartographer: From Mapping to Reasoning Over Counterfactual Worlds
Gaël Gendron, Jože M. Rožanec, Michael Witbrock +1
Causal world models are systems that can answer counterfactual questions about an environment of interest, i.e. predict how it would have evolved if an arbitrary subset of events h…
Counterfactual Causal Inference in Natural Language with Large Language Models
Gaël Gendron, Jože M. Rožanec, Michael Witbrock +1
Causal structure discovery methods are commonly applied to structured data where the causal variables are known and where statistical testing can be used to assess the causal relat…
Robust Domain Generalisation with Causal Invariant Bayesian Neural Networks
Gaël Gendron, Michael Witbrock, Gillian Dobbie
Deep neural networks can obtain impressive performance on various tasks under the assumption that their training domain is identical to their target domain. Performance can drop dr…
Recurrence over Video Frames (RoVF) for the Re-identification of Meerkats
Mitchell Rogers, Kobe Knowles, Gaël Gendron +7
Deep learning approaches for animal re-identification have had a major impact on conservation, significantly reducing the time required for many downstream tasks, such as well-bein…
Can Large Language Models Learn Independent Causal Mechanisms?
Gaël Gendron, Bao Trung Nguyen, Alex Yuxuan Peng +2
Despite impressive performance on language modelling and complex reasoning tasks, Large Language Models (LLMs) fall short on the same tasks in uncommon settings or with distributio…
Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks
Gaël Gendron, Yang Chen, Mitchell Rogers +11
Better understanding the natural world is a crucial task with a wide range of applications. In environments with close proximity between humans and animals, such as zoos, it is ess…
Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning
Qiming Bao, Gael Gendron, Alex Yuxuan Peng +5
Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-li…
Meerkat Behaviour Recognition Dataset
Mitchell Rogers, Gaël Gendron, David Arturo Soriano Valdez +10
Recording animal behaviour is an important step in evaluating the well-being of animals and further understanding the natural world. Current methods for documenting animal behaviou…
Large Language Models Are Not Strong Abstract Reasoners
Gaël Gendron, Qiming Bao, Michael Witbrock +1
Large Language Models have shown tremendous performance on a large variety of natural language processing tasks, ranging from text comprehension to common sense reasoning. However,…
Disentanglement of Latent Representations via Causal Interventions
Gaël Gendron, Michael Witbrock, Gillian Dobbie
The process of generating data such as images is controlled by independent and unknown factors of variation. The retrieval of these variables has been studied extensively in the di…
A Survey of Methods, Challenges and Perspectives in Causality
Gaël Gendron, Michael Witbrock, Gillian Dobbie
Deep Learning models have shown success in a large variety of tasks by extracting correlation patterns from high-dimensional data but still struggle when generalizing out of their…
Relating Blindsight and AI: A Review
Joshua Bensemann, Qiming Bao, Gaël Gendron +2
Processes occurring in brains, a.k.a. biological neural networks, can and have been modeled within artificial neural network architectures. Due to this, we have conducted a review…