6 citations · 27 across the 17 of their papers we have counts for
5 papers · 1 filter
Multi-View Causal Representation Learning with Partial Observability
Dingling Yao, Danru Xu, Sébastien Lachapelle +5
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities. We allow a partial…
Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning
Fan Feng, Sara Magliacane
In many reinforcement learning tasks, the agent has to learn to interact with many objects of different types and generalize to unseen combinations and numbers of objects. Often a…
BISCUIT: Causal Representation Learning from Binary Interactions
Phillip Lippe, Sara Magliacane, Sindy Löwe +3
Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and embodied AI. While an agent can commonly inter…
Graph Switching Dynamical Systems
Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas +1
Dynamical systems with complex behaviours, e.g. immune system cells interacting with a pathogen, are commonly modelled by splitting the behaviour into different regimes, or modes,…
Modulated Neural ODEs
Ilze Amanda Auzina, Çağatay Yıldız, Sara Magliacane +2
Neural ordinary differential equations (NODEs) have been proven useful for learning non-linear dynamics of arbitrary trajectories. However, current NODE methods capture variations…