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BISCUIT: Causal Representation Learning from Binary Interactions

Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and …

Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves

Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems

Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional …

Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves

Graph Switching Dynamical Systems in Interactive Systems

Dynamical systems with complex behaviours, e.g. immune system cells interacting with a pathogen, are commonly modelled by splitting the …

Yongtuo Liu, Sara Magliacane, Miltos Kofinas, Efstratios Gavves

Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning

In many reinforcement learning tasks, the agent has to learn to interact with many objects of different types and generalize to unseen …

Fan Feng, Sara Magliacane

Modulated Neural ODEs

Neural ordinary differential equations (NODEs) have been proven useful for learning non-linear dynamics of arbitrary trajectories. …

Ilze Amanda Auzina, Cagatay Yildiz, Sara Magliacane, Matthias Bethge, Efstratios Gavves

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

One practical challenge in reinforcement learning (RL) is how to make quick adaptations when faced with new environments. In this …

Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

Factored Adaptation for Non-Stationary Reinforcement Learning

Dealing with non-stationarity in environments (e.g., in the transition dynamics) and objectives (e.g., in the reward functions) is a …

Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane

Intervention Design for Causal Representation Learning

In this paper, we take a first step towards bringing two fields of causality closer together, intervention design and causal …

Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves

Verifiably safe exploration for end-to-end reinforcement learning

Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during …

Nathan Hunt, Nathan Fulton, Sara Magliacane, Trong Nghia Hoang, Subhro Das, Armando Solar-Lezama

Active Structure Learning of Causal DAGs via Directed Clique Trees

A growing body of work has begun to study intervention design for efficient structure learning of causal directed acyclic graphs …

Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam