| Towards a unified analysis of random Fourier features Z Li, JF Ton, D Oglic, D Sejdinovic International conference on machine learning, 3905-3914, 2019 | 151 | 2019 |
| Towards a unified analysis of random fourier features Z Li, JF Ton, D Oglic, D Sejdinovic Journal of Machine Learning Research 22 (108), 1-51, 2021 | 56 | 2021 |
| Optimal rates for regularized conditional mean embedding learning Z Li, D Meunier, M Mollenhauer, A Gretton Advances in Neural Information Processing Systems 35, 4433-4445, 2022 | 46 | 2022 |
| Towards an understanding of benign overfitting in neural networks Z Li, ZH Zhou, A Gretton arXiv preprint arXiv:2106.03212, 2021 | 43 | 2021 |
| Kernel dependence regularizers and gaussian processes with applications to algorithmic fairness Z Li, A Perez-Suay, G Camps-Valls, D Sejdinovic arXiv preprint arXiv:1911.04322, 2019 | 25 | 2019 |
| Benign overfitting and noisy features Z Li, WJ Su, D Sejdinovic Journal of the American Statistical Association 118 (544), 2876-2888, 2023 | 21 | 2023 |
| Physics-aware nonparametric regression models for Earth data analysis J Cortés-Andrés, G Camps-Valls, S Sippel, E Székely, D Sejdinovic, ... Environmental Research Letters 17 (5), 054034, 2022 | 15 | 2022 |
| Nonlinear meta-learning can guarantee faster rates D Meunier, Z Li, A Gretton, S Kpotufe arXiv preprint arXiv:2307.10870, 2023 | 7 | 2023 |
| Towards optimal Sobolev norm rates for the vector-valued regularized least-squares algorithm Z Li, D Meunier, M Mollenhauer, A Gretton Journal of Machine Learning Research 25 (181), 1-51, 2024 | 5 | 2024 |
| Sharp analysis of random fourier features in classification Z Li Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7444-7452, 2022 | 4 | 2022 |
| Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms D Meunier, Z Shen, M Mollenhauer, A Gretton, Z Li arXiv preprint arXiv:2405.14778, 2024 | 1 | 2024 |
| Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning B Chen, J Liu, Z Li arXiv preprint arXiv:2408.16577, 2024 | | 2024 |
| Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression J Kim, D Meunier, A Gretton, T Suzuki, Z Li NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning, 0 | | |
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideПотвърден имейл адрес: adelaide.edu.au
