Dingling Yao
6 Publications
2025 | Published | Conference Paper | IST-REx-ID: 20032 | 
J. Chen, D. Yao, A. A. Pervez, D.-A. Alistarh, and F. Locatello, “Scalable mechanistic neural networks,” in 13th International Conference on Learning Representations, Singapore, Singapore, 2025, pp. 63716–63737.
[Published Version]
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20592 |
D. Yao, F. Tronarp, and N. Bosch, “Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers,” in Proceedings of the 1st International Conference on Probabilistic Numerics, Sophia Antipolis, France, 2025, vol. 271.
[Preprint]
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 14946 |
D. Yao et al., “Multi-view causal representation learning with partial observability,” in 12th International Conference on Learning Representations, Vienna, Austria, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19010 |
D. Yao, D. Rancati, R. Cadei, M. Fumero, and F. Locatello, “Unifying causal representation learning with the invariance principle,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
[Published Version]
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19005 |
D. Yao, C. J. Muller, and F. Locatello, “Marrying causal representation learning with dynamical systems for science,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14958 |
D. Xu et al., “A sparsity principle for partially observable causal representation learning,” in Causal Representation Learning Workshop at NeurIPS 2023, New Orleans, LA, United States, 2023.
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Grants
6 Publications
2025 | Published | Conference Paper | IST-REx-ID: 20032 |
J. Chen, D. Yao, A. A. Pervez, D.-A. Alistarh, and F. Locatello, “Scalable mechanistic neural networks,” in 13th International Conference on Learning Representations, Singapore, Singapore, 2025, pp. 63716–63737.
[Published Version]
View
| Files available
| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20592 |
D. Yao, F. Tronarp, and N. Bosch, “Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers,” in Proceedings of the 1st International Conference on Probabilistic Numerics, Sophia Antipolis, France, 2025, vol. 271.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 14946 |
D. Yao et al., “Multi-view causal representation learning with partial observability,” in 12th International Conference on Learning Representations, Vienna, Austria, 2024.
[Published Version]
View
| Files available
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19010 |
D. Yao, D. Rancati, R. Cadei, M. Fumero, and F. Locatello, “Unifying causal representation learning with the invariance principle,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
[Published Version]
View
| Files available
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19005 |
D. Yao, C. J. Muller, and F. Locatello, “Marrying causal representation learning with dynamical systems for science,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
[Published Version]
View
| Files available
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14958 |
D. Xu et al., “A sparsity principle for partially observable causal representation learning,” in Causal Representation Learning Workshop at NeurIPS 2023, New Orleans, LA, United States, 2023.
[Published Version]
View
| Files available
| Download Published Version (ext.)