no code implementations • 26 Mar 2023 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
However, such a learning rate is typically considered to be ``slow", compared to a ``fast rate" of $O(\lambda/n)$ in many learning scenarios.
no code implementations • 9 Mar 2023 • Junzhang Jia, Xuetong Wu, Jingge Zhu, Jamie Evans
We study the effectiveness of stochastic side information in deterministic online learning scenarios.
no code implementations • 12 Jul 2022 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
Specifically, we provide generalization error upper bounds for the empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase.
no code implementations • 10 May 2022 • Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
We show that in causal learning, the excess risk depends on the size of the source sample at a rate of O(1/m) only if the labelling distribution between the source and target domains remains unchanged.
no code implementations • 6 May 2022 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
However, such a learning rate is typically considered to be "slow", compared to a "fast rate" of O(1/n) in many learning scenarios.
no code implementations • 3 Sep 2021 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem.
no code implementations • 4 May 2021 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
On the one hand, it is conceivable that knowledge from one task could be useful for solving a related problem.
no code implementations • 1 Dec 2020 • Xuetong Wu, Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Zobaida Edib, Michelle Peate
Also, missing values are unavoidable in health and medical datasets and tackling the problem arising from the inadequate instances and missingness is not straightforward (Snell, et al. 2017, Sterne, et al. 2009).
no code implementations • 16 Nov 2020 • Xuetong Wu, Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Zobaida Edib, Michelle Peate
Clinical decision support using data mining techniques offers more intelligent way to reduce the decision error in the last few years.
no code implementations • 18 May 2020 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
Specifically, we provide generalization error upper bounds for general transfer learning algorithms and extend the results to a specific empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase.