Dingling Yao
Graduate School
Locatello Group
5 Publications
2025 | Published | Conference Paper | IST-REx-ID: 20032 | 
Chen, J., Yao, D., Pervez, A. A., Alistarh, D.-A., & Locatello, F. (2025). Scalable mechanistic neural networks. In 13th International Conference on Learning Representations (pp. 63716–63737). Singapore, Singapore: OpenReview.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 14946 |
Yao, D., Xu, D., Lachapelle, S., Magliacane, S., Taslakian, P., Martius, G., … Locatello, F. (2024). Multi-view causal representation learning with partial observability. In 12th International Conference on Learning Representations. Vienna, Austria: Curran Associates.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19010 |
Yao, D., Rancati, D., Cadei, R., Fumero, M., & Locatello, F. (2024). Unifying causal representation learning with the invariance principle. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19005 |
Yao, D., Muller, C. J., & Locatello, F. (2024). Marrying causal representation learning with dynamical systems for science. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14958 |
Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello, F., & Magliacane, S. (2023). A sparsity principle for partially observable causal representation learning. In Causal Representation Learning Workshop at NeurIPS 2023. New Orleans, LA, United States: OpenReview.
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Grants
5 Publications
2025 | Published | Conference Paper | IST-REx-ID: 20032 |
Chen, J., Yao, D., Pervez, A. A., Alistarh, D.-A., & Locatello, F. (2025). Scalable mechanistic neural networks. In 13th International Conference on Learning Representations (pp. 63716–63737). Singapore, Singapore: OpenReview.
[Published Version]
View
| Files available
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 14946 |
Yao, D., Xu, D., Lachapelle, S., Magliacane, S., Taslakian, P., Martius, G., … Locatello, F. (2024). Multi-view causal representation learning with partial observability. In 12th International Conference on Learning Representations. Vienna, Austria: Curran Associates.
[Published Version]
View
| Files available
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19010 |
Yao, D., Rancati, D., Cadei, R., Fumero, M., & Locatello, F. (2024). Unifying causal representation learning with the invariance principle. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Published Version]
View
| Files available
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19005 |
Yao, D., Muller, C. J., & Locatello, F. (2024). Marrying causal representation learning with dynamical systems for science. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Published Version]
View
| Files available
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14958 |
Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello, F., & Magliacane, S. (2023). A sparsity principle for partially observable causal representation learning. In Causal Representation Learning Workshop at NeurIPS 2023. New Orleans, LA, United States: OpenReview.
[Published Version]
View
| Files available
| Download Published Version (ext.)