Publications

Daniel Gallo Fernandez, Robert van der Klis, Razvan-Andrei Matișan, Janusz Partyka, Samuele Papa, Efstratios Gavves, Phillip Lippe (2024). NARAIM - Native Aspect Ratio Autoregressive Image Models. NeurIPS 2024 Workshop on Self-Supervised Learning - Theory and Practice.

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John Gkountouras, Matthias Lindemann, Phillip Lippe, Efstratios Gavves, Ivan Titov (2024). Language Agents Meet Causality - Bridging LLMs and Causal World Models. arXiv preprint.

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Samuele Papa, Riccardo Valperga, David M Knigge, Miltiadis Kofinas, Phillip Lippe, Jan-jakob Sonke, Efstratios Gavves (2024). How to Train Neural Field Representations - A Comprehensive Study and Benchmark. Conference on Computer Vision and Pattern Recognition 2024.

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Davide Talon, Phillip Lippe, Stuart James, Alessio Del Bue, Sara Magliacane (2024). Towards the Reusability and Compositionality of Causal Representations. 3rd Causal Learning and Reasoning.

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Angelos Nalmpantis, Phillip Lippe, Sara Magliacane (2023). Hierarchical Causal Representation Learning. NeurIPS 2023 Workshop on Causal Representation Learning.

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Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter (2023). PDE-Refiner - Achieving Accurate Long Rollouts with Neural PDE Solvers. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 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. Uncertainty in Artificial Intelligence (UAI 2023) - Spotlight.

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Sindy Löwe, Phillip Lippe, Francesco Locatello, Max Welling (2023). Rotating Features for Object Discovery. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 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. International Conference on Learning Representations (ICLR 2023).

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Adeel Pervez, Phillip Lippe, Efstratios Gavves (2023). Scalable Subset Sampling with Neural Conditional Poisson Networks. International Conference on Learning Representations (ICLR 2023).

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Adeel Pervez, Phillip Lippe, Efstratios Gavves (2023). Differentiable Mathematical Programming for Object-Centric Representation Learning. International Conference on Learning Representations (ICLR 2023).

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

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Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves (2022). CITRIS - Causal Identifiability from Temporal Intervened Sequences. International Conference on Machine Learning (ICML 2022).

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Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling (2022). Complex-Valued Autoencoders for Object Discovery. Transactions on Machine Learning Research.

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Johann Brehmer, Pim De Haan, Phillip Lippe, Taco Cohen (2022). Weakly supervised causal representation learning. Advances in Neural Information Processing Systems (NeurIPS 2022).

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Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova (2022). Meta-learning for fast cross-lingual adaptation in dependency parsing. ACL Long paper, 2022.

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Julian Suk, Pim De Haan, Phillip Lippe, Christoph Brune, Jelmer M. Wolterink (2021). Mesh convolutional neural networks for wall shear stress estimation in 3D artery models. Statistical Atlases and Computational Models of the Heart (STACOM), 2021.

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Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Casey A. Fitzpatrick, Peter Bull, Greg Lipstein, Tony Nelli, Ron Zhu, Niklas Muennighoff, Rize Velioglu, Jewgeni Rose, Phillip Lippe, Nithin Holla, Shantanu Chandra, Santhosh Rajamanickam, Georgios Antoniou, Ekaterina Shutova, Helen Yannakoudakis, Vlad Sandulescu, Umut Ozertem, Patrick Pantel, Lucia Specia, Devi Parikh (2021). The Hateful Memes Challenge: Competition Report. Proceedings of Machine Learning Research.

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Phillip Lippe, Taco Cohen, Efstratios Gavves (2021). Efficient Neural Causal Discovery without Acyclicity Constraints. International Conference on Learning Representations (ICLR 2022).

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Phillip Lippe, Efstratios Gavves (2021). Categorical Normalizing Flows via Continuous Transformations. International Conference on Learning Representations (ICLR 2021).

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Phillip Lippe, Nithin Holla, Shantanu Chandra, Santhosh Rajamanickam, Georgios Antoniou, Ekaterina Shutova, Helen Yannakoudakis (2020). A Multimodal Framework for the Detection of Hateful Memes. arXiv preprint arXiv:2012.12871.

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Phillip Lippe, Pengjie Ren, Hinda Haned, Bart Voorn, Maarten de Rijke (2020). Simultaneously Improving Utility and User Experience in Task-oriented Dialogue Systems. SIGIR Workshop on eCommerce (eCOM), 2022.

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Phillip Lippe, Stephan Schulz (2018). Deep Reasoning - Hardware Accelerated Artificial Intelligence. Deduktionstreffen 2018, LuxLogAI.

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