Dingling Yao
6 Publications
2025 | Published | Conference Paper | IST-REx-ID: 20032 | 
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.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20592 |
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.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 14946 |
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.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19010 |
Yao D, Rancati D, Cadei R, Fumero M, Locatello F. Unifying causal representation learning with the invariance principle. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19005 |
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.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14958 |
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.
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Grants
6 Publications
2025 | Published | Conference Paper | IST-REx-ID: 20032 |
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
2025 | Published | Conference Paper | IST-REx-ID: 20592 |
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
2024 | Published | Conference Paper | IST-REx-ID: 14946 |
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
2024 | Published | Conference Paper | IST-REx-ID: 19010 |
Yao D, Rancati D, Cadei R, Fumero M, Locatello F. Unifying causal representation learning with the invariance principle. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Published Version]
View
| Files available
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19005 |
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
2023 | Published | Conference Paper | IST-REx-ID: 14958 |
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.)