Recent publications
Article
Huang, Z, Lei, Y & Kaban, A 2023, 'Optimisation and Learning with Randomly Compressed Gradient Updates', Neural Computation.
Lei, Y, Hu, T & Tang, K 2021, 'Generalization performance of multi-pass stochastic gradient descent with convex loss functions', Journal of Machine Learning Research, vol. 22, 25. <https://jmlr.org/papers/v22/19-716.html>
Lei, Y & Tang, K 2021, 'Learning rates for stochastic gradient descent with nonconvex objectives', IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 12, pp. 4505-4511. https://doi.org/10.1109/TPAMI.2021.3068154
Lei, Y & Ying, Y 2021, 'Stochastic proximal AUC maximization', Journal of Machine Learning Research, vol. 22, 61. <https://jmlr.org/papers/volume22/19-418/19-418.pdf>
Lei, Y & Zhou, D-X 2020, 'Convergence of Online Mirror Descent', Applied and Computational Harmonic Analysis, vol. 48, no. 1, pp. 343-373.
Conference contribution
Huang, Z, Lei, Y & Kaban, A 2022, Noise-efficient learning of differentially private partitioning machine ensembles: noise reduction in private forests. in M-R Amin, S Canu, A Fischer, T Guns, PK Novak & G Tsoumakas (eds), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. A Springer Nature Computer Science book series (CCIS, LNAI, LNBI, LNBIP or LNCS, Springer, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Genoble, France, 19/09/22.
Mustafa, W, Lei, Y & Kloft, M 2022, On the generalization analysis of adversarial learning. in K Chaudhuri, S Jegelka, L Song, C Szepesvari, G Niu & S Sabato (eds), International Conference on Machine Learning, 17-23 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research, vol. 162, Proceedings of Machine Learning Research, pp. 16174-16196, Thirty-ninth International Conference on Machine Learning, Baltimore , Maryland, United States, 17/07/22. <https://proceedings.mlr.press/v162/mustafa22a.html>
Ledent, A, Alves, R, Lei, Y & Kloft, M 2021, Fine-grained generalization analysis of inductive matrix completion. in M Ranzato, A Beygelzimer, PS Liang, JW Vaughan & Y Dauphin (eds), Advances in Neural Information Processing Systems 34. Advances in neural information processing systems, NeurIPS, Thirty-fifth Conference on Neural Information Processing Systems, 6/12/21. <https://proceedings.neurips.cc/paper/2021/hash/d6428eecbe0f7dff83fc607c5044b2b9-Abstract.html>
Wu, L, Ledent, A, Lei, Y & Kloft, M 2021, Fine-grained generalization analysis of vector-valued learning. in AAAI'21 Proceedings of the Thirty-fifth AAAI Conference on Artificial Intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, no. 12, vol. 35, AAAI Press, pp. 10338-10346, 35th AAAI Conference on Artificial Intelligence, Vancouver, Canada, 2/02/21. <https://ojs.aaai.org/index.php/AAAI/article/view/17238>
Lei, Y, Liu, M & Ying, Y 2021, Generalization guarantee of SGD for pairwise learning. in M Ranzato, A Beygelzimer, PS Liang, JW Vaughan & Y Dauphin (eds), Advances in Neural Information Processing Systems 34. Advances in neural information processing systems, NeurIPS, Thirty-fifth Conference on Neural Information Processing Systems, 6/12/21. <https://proceedings.neurips.cc/paper/2021/hash/b1301141feffabac455e1f90a7de2054-Abstract.html>
Ledent, A, Mustafa, W, Lei, Y & Kloft, M 2021, Norm-based generalisation bounds for deep multi-class convolutional neural networks. in AAAI'21 Proceedings of the Thirty-fifth AAAI Conference on Artificial Intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, no. 9, vol. 35, AAAI Press, pp. 8279-8287, 35th AAAI Conference on Artificial Intelligence, Vancouver, Canada, 2/02/21. <https://ojs.aaai.org/index.php/AAAI/article/view/17007>
Lei, Y & Ying, Y 2021, Sharper generalization bounds for learning with gradient-dominated objective functions. in International Conference on Learning Representations: ICLR 2021. OpenReview.net, pp. 1-23, The Ninth International Conference on Learning Representations, 3/05/21. <https://openreview.net/forum?id=r28GdiQF7vM>
Yang, Z, Lei, Y, Lyu, S & Ying, Y 2021, Stability and differential privacy of stochastic gradient descent for pairwise learning with non-smooth loss. in A Banerjee & K Fukumizu (eds), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 130, pp. 2026-2034, The 24th International Conference on Artificial Intelligence and Statistics, 13/04/21. <http://proceedings.mlr.press/v130/yang21c.html>
Lei, Y, Yang, Z, Yang, T & Ying, Y 2021, Stability and generalization of stochastic gradient methods for minimax problems. in M Meila & T Zhang (eds), Proceedings of ICML 2021. Proceedings of Machine Learning Research, vol. 139, JMLR , pp. 6175-6186, The Thirty-eighth International Conference on Machine Learning , 18/07/21. <http://proceedings.mlr.press/v139/lei21a.html>
Lei, Y & Ying, Y 2020, Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent. in International Conference on Machine Learning.
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