Recent Publications

Here is a selected list of recent publications.

For all my publications, see here: https://scholar.google.co.uk/citations?user=H3j_zQ4AAAAJ

Ilze Amanda Auzina, Cagatay Yildiz, Sara Magliacane, Matthias Bethge, Efstratios Gavves (2023). Modulated Neural ODEs. NeurIPS 2023.

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Fan Feng, Sara Magliacane (2023). Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning. NeurIPS 2023.

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Yongtuo Liu, Sara Magliacane, Miltos Kofinas, Efstratios Gavves (2023). Graph Switching Dynamical Systems in Interactive Systems. ICML 2023.

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Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves (2023). Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems. ICLR 2023.

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Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves (2023). BISCUIT: Causal Representation Learning from Binary Interactions. UAI 2023.

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Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Stratis Gavves (2022). CITRIS: Causal Identifiability from Temporal Intervened Sequences. ICML 2022.

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Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves (2022). Intervention Design for Causal Representation Learning. UAI 2022 Workshop on Causal Representation Learning.

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Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane (2022). Factored Adaptation for Non-Stationary Reinforcement Learning. Advances in Neural Information Processing Systems.

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Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang (2022). AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. International Conference on Learning Representations.

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Nathan Hunt, Nathan Fulton, Sara Magliacane, Trong Nghia Hoang, Subhro Das, Armando Solar-Lezama (2021). Verifiably safe exploration for end-to-end reinforcement learning. Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control.

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Joris M. Mooij, Sara Magliacane, Tom Claassen (2020). Joint Causal Inference from Multiple Contexts. J. Mach. Learn. Res..

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Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam (2020). Active Structure Learning of Causal DAGs via Directed Clique Trees. Advances in Neural Information Processing Systems.

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Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adsera, Guy Bresler (2019). Sample Efficient Active Learning of Causal Trees. Advances in Neural Information Processing Systems.

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Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M Mooij (2018). Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. Advances in Neural Information Processing Systems.

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