Dingling Yao

7 Publications

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[7]
2025 | Published | Conference Paper | IST-REx-ID: 20032 | OA
Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. Scalable mechanistic neural networks. In: 13th International Conference on Learning Representations. ICLR; 2025:63716-63737.
[Published Version] View | Files available | arXiv
 
[6]
2025 | Published | Conference Paper | IST-REx-ID: 20592 | OA
Yao D, Tronarp F, Bosch N. Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers. In: Proceedings of the 1st International Conference on Probabilistic Numerics. Vol 271. ML Research Press; 2025.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[5]
2025 | Published | Conference Paper | IST-REx-ID: 19010 | OA
Yao D, Rancati D, Cadei R, Fumero M, Locatello F. Unifying causal representation learning with the invariance principle. In: 13th International Conference on Learning Representations. ICLR; 2025.
[Published Version] View | Files available | arXiv
 
[4]
2025 | Epub ahead of print | Conference Paper | IST-REx-ID: 21068 | OA
Yao D, Huang S, Cadei R, Zhang K, Locatello F. The third pillar of causal analysis? A measurement perspective on causal representations. In: 39th Annual Conference on Neural Information Processing Systems. Vol 38. Neural Information Processing Systems Foundation; 2025.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[3]
2024 | Published | Conference Paper | IST-REx-ID: 19005 | OA
Yao D, Muller CJ, Locatello F. Marrying causal representation learning with dynamical systems for science. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Published Version] View | Files available | arXiv
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 14946 | OA
Yao D, Xu D, Lachapelle S, et al. Multi-view causal representation learning with partial observability. In: 12th International Conference on Learning Representations. Curran Associates; 2024.
[Published Version] View | Files available | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 14958 | OA
Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable causal representation learning. In: Causal Representation Learning Workshop at NeurIPS 2023. OpenReview; 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 
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7 Publications

Mark all

[7]
2025 | Published | Conference Paper | IST-REx-ID: 20032 | OA
Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. Scalable mechanistic neural networks. In: 13th International Conference on Learning Representations. ICLR; 2025:63716-63737.
[Published Version] View | Files available | arXiv
 
[6]
2025 | Published | Conference Paper | IST-REx-ID: 20592 | OA
Yao D, Tronarp F, Bosch N. Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers. In: Proceedings of the 1st International Conference on Probabilistic Numerics. Vol 271. ML Research Press; 2025.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[5]
2025 | Published | Conference Paper | IST-REx-ID: 19010 | OA
Yao D, Rancati D, Cadei R, Fumero M, Locatello F. Unifying causal representation learning with the invariance principle. In: 13th International Conference on Learning Representations. ICLR; 2025.
[Published Version] View | Files available | arXiv
 
[4]
2025 | Epub ahead of print | Conference Paper | IST-REx-ID: 21068 | OA
Yao D, Huang S, Cadei R, Zhang K, Locatello F. The third pillar of causal analysis? A measurement perspective on causal representations. In: 39th Annual Conference on Neural Information Processing Systems. Vol 38. Neural Information Processing Systems Foundation; 2025.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[3]
2024 | Published | Conference Paper | IST-REx-ID: 19005 | OA
Yao D, Muller CJ, Locatello F. Marrying causal representation learning with dynamical systems for science. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Published Version] View | Files available | arXiv
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 14946 | OA
Yao D, Xu D, Lachapelle S, et al. Multi-view causal representation learning with partial observability. In: 12th International Conference on Learning Representations. Curran Associates; 2024.
[Published Version] View | Files available | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 14958 | OA
Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable causal representation learning. In: Causal Representation Learning Workshop at NeurIPS 2023. OpenReview; 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 
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