Zhu Li (Michael)

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Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
Dimitri MeunierPhD student, Gatsby Unit, UCLVerified email at ucl.ac.uk
Dino OglicAstraZeneca CambridgeVerified email at astrazeneca.com
Jean-Francois TonByteDance ResearchVerified email at bytedance.com
Mattes MollenhauerMerantix MomentumVerified email at merantix-momentum.com
Gustau Camps-VallsProfessor. Image Processing Lab (IPL). Universitat de ValènciaVerified email at uv.es
Weijie SuAssociate Professor, University of PennsylvaniaVerified email at wharton.upenn.edu
Zhi-Hua ZhouNanjing UniversityVerified email at nju.edu.cn
Samory KpotufeColumbia UniversityVerified email at columbia.edu
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Juno KIMUniversity of TokyoVerified email at g.ecc.u-tokyo.ac.jp
Mengyue YangAssistant Professor, University of BristolVerified email at bristol.ac.uk
Timothy ChristensenYale UniversityVerified email at yale.edu

Zhu Li (Michael)

University College London

Verified email at ucl.ac.uk - Homepage

Machine Learning Kernel Methods Learning Theory


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Towards a unified analysis of random Fourier features Z Li, JF Ton, D Oglic, D Sejdinovic International conference on machine learning, 3905-3914, 2019	152	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	64	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	52	2022
Towards an understanding of benign overfitting in neural networks Z Li, ZH Zhou, A Gretton arXiv preprint arXiv:2106.03212, 2021	47	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	24	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	18	2022
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	8	2024
Nonlinear meta-learning can guarantee faster rates D Meunier, Z Li, A Gretton, S Kpotufe arXiv preprint arXiv:2307.10870, 2023	6	2023
Sharp analysis of random fourier features in classification Z Li Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7444-7452, 2022	5	2022
Optimal rates for vector-valued spectral regularization learning algorithms D Meunier, Z Shen, M Mollenhauer, A Gretton, Z Li Advances in Neural Information Processing Systems 37, 82514-82559, 2025	3	2025
Nonparametric instrumental regression via kernel methods is minimax optimal D Meunier, Z Li, T Christensen, A Gretton arXiv preprint arXiv:2411.19653, 2024	2	2024
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression J Kim, D Meunier, A Gretton, T Suzuki, Z Li arXiv preprint arXiv:2501.04898, 2025		2025
Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning B Chen, J Liu, Z Li, M Yang arXiv preprint arXiv:2408.16577, 2024		2024

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