2 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Benchmarking World Models for Continual Learning on Compositional Tasks
Haoyu Zhou, Joe Watson, Anson Lei +1
A desirable property of a world model is the ability to learn continually across tasks, adapting to new environments without forgetting what the agent has already learnt. In partic…
From Observation to Insight: Mechanistic World Models and the Quest for Autonomous Discovery
Ingmar Posner, Anson Lei, Bernhard Schölkopf
Recent advances in foundation models have transformed AI for Science, enabling remarkably accurate predictive performance across domains ranging from protein folding to weather for…
Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models
Tianyou Wang, Anson Lei, Joe Watson +1
Imitation learning has emerged as a powerful paradigm for learning visuomotor policies, but its generalisation and stability are limited by the scale and quality of demonstration d…
No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
Bradley Stanley-Clamp, Anson Lei, Hannah M. Christensen +1
Climate emulation is an out-of-distribution (OOD) projection task. This is precisely the challenge where modern Machine Learning (ML) methods are most prone to failure. Consequentl…
Disentangling Dynamical Systems: Causal Representation Learning Meets Local Sparse Attention
Markus W. Baumgartner, Anson Lei, Joe Watson +1
Parametric system identification methods estimate the parameters of explicitly defined physical systems from data. Yet, they remain constrained by the need to provide an explicit f…