| 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 (ICLR), 2023 | 18 | 2023 |
| Spike-inspired rank coding for fast and accurate recurrent neural networks A Jeffares, Q Guo, P Stenetorp, T Moraitis International Conference on Learning Representations (ICLR), 2022 | 16 | 2022 |
| 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 | 12 | 2023 |
| Improving adaptive conformal prediction using self-supervised learning N Seedat*, A Jeffares*, F Imrie, M van der Schaar International Conference on Artificial Intelligence and Statistics (AISTATS …, 2023 | 11 | 2023 |
| A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning A Curth*, A Jeffares*, M van der Schaar Neural Information Processing Systems (NeurIPS), 2023 | 10 | 2023 |
| Why do random forests work? Understanding tree ensembles as self-regularizing adaptive smoothers A Curth, A Jeffares, M van der Schaar arXiv preprint arXiv:2402.01502, 2024 | 4 | 2024 |
| Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise T Pouplin*, A Jeffares*, N Seedat, M van der Schaar International Conference on Machine Learning (ICML), 2024 | | 2024 |
| Looking at Deep Learning Phenomena Through a Telescoping Lens A Jeffares, A Curth, M van der Schaar High-dimensional Learning Dynamics 2024: The Emergence of Structure and …, 0 | | |
Mihaela van der SchaarUniversity of Cambridge, The Alan Turing InstituteVerified email at ee.ucla.edu