| Hyperimpute: Generalized Iterative Imputation with Automatic Model Selection D Jarrett*, B Cebere*, T Liu, A Curth, M van der Schaar International Conference on Machine Learning, 9916-9937, 2022 | 75 | 2022 |
| Epileptic Seizure Classification with Symmetric and Hybrid Bilinear Models T Liu, ND Truong, A Nikpour, L Zhou, O Kavehei IEEE Journal of Biomedical and Health Informatics 24 (10), 2844-2851, 2020 | 71 | 2020 |
| GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure T Liu, Z Qian, J Berrevoets, M van der Schaar International Conference on Learning Representations, 2023 | 61 | 2023 |
| Large Language Models to Enhance Bayesian Optimization T Liu*, N Astorga*, N Seedat, M van der Schaar The Twelfth International Conference on Learning Representations, 2024 | 50 | 2024 |
| TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization A Jeffares*, T Liu*, J Crabbé, F Imrie, M van der Schaar International Conference on Learning Representations, 2023 | 30 | 2023 |
| Joint Training of Deep Ensembles Fails Due to Learner Collusion A Jeffares, T Liu, J Crabbé, M van der Schaar Neural Information Processing Systems (NeurIPS), 2023 | 21 | 2023 |
| Synthetic Data in Biomedicine via Generative Artificial Intelligence B van Breugel, T Liu, D Oglic, M van der Schaar Nature Reviews Bioengineering, 1-14, 2024 | 6 | 2024 |
| Partially observable cost-aware active-learning with large language models N Astorga, T Liu, N Seedat, M van der Schaar The Thirty-Eighth Annual Conference on Neural Information Processing Systems, 2024 | 6 | 2024 |
| Autoformulation of Mathematical Optimization Models Using LLMs N Astorga*, T Liu*, Y Xiao, M van der Schaar arXiv preprint arXiv:2411.01679, 2024 | 4 | 2024 |
| Towards Transparent Time Series Forecasting K Kacprzyk, T Liu, M van der Schaar The Twelfth International Conference on Learning Representations, 2024 | 4 | 2024 |
| Automatically Learning Hybrid Digital Twins of Dynamical Systems S Holt*, T Liu*, M van der Schaar Neural Information Processing Systems (NeurIPS), 2024 | 3 | 2024 |
| Unveiling the power of sparse neural networks for feature selection Z Atashgahi, T Liu, M Pechenizkiy, R Veldhuis, DC Mocanu, ... arXiv preprint arXiv:2408.04583, 2024 | 2 | 2024 |
| Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes T Liu, AJ Chan, B van Breugel, M van der Schaar Algorithmic Fairness through the Lens of Causality and Privacy at NeurIPS 2022, 2022 | 2 | 2022 |
| Improving llm generation with inverse and forward alignment: Reward modeling, prompting, fine-tuning, and inference-time optimization H Sun, T Pouplin, N Astorga, T Liu, M van der Schaar The First Workshop on System-2 Reasoning at Scale, NeurIPS'24, 0 | 2 | |
| Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios N Astorga, T Liu, N Seedat, M van der Schaar Advances in Neural Information Processing Systems 37, 20819-20857, 2025 | 1 | 2025 |
| Data-driven discovery of dynamical systems in pharmacology using large language models S Holt, Z Qian, T Liu, J Weatherall, M van der Schaar Advances in Neural Information Processing Systems 37, 96325-96366, 2025 | 1 | 2025 |
| Learning Representations Without Compositional Assumptions T Liu, J Berrevoets, Z Qian, M Van Der Schaar International Conference on Machine Learning, 21388-21403, 2023 | 1 | 2023 |
| Active Task Disambiguation with LLMs K Kobalczyk, N Astorga, T Liu, M van der Schaar arXiv preprint arXiv:2502.04485, 2025 | | 2025 |
| Decision Tree Induction via Semantically-Aware Evolution T Liu, N Huynh, M van der Schaar The Thirteenth International Conference on Learning Representations, 0 | | |