8 citations · 15 across the 10 of their papers we have counts for
10 papers
On the Value of Stochastic Side Information in Online Learning
Junzhang Jia, Xuetong Wu, Jingge Zhu +1
We study the effectiveness of stochastic side information in deterministic online learning scenarios. We propose a forecaster to predict a deterministic sequence where its performa…
Fast Rate Information-theoretic Bounds on Generalization Errors
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
The generalization error of a learning algorithm refers to the discrepancy between the loss of a learning algorithm on training data and that on unseen testing data. Various inform…
On the Generalization for Transfer Learning: An Information-Theoretic Analysis
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different probability distributions. In t…
Fast Rate Generalization Error Bounds: Variations on a Theme
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
A recent line of works, initiated by Russo and Xu, has shown that the generalization error of a learning algorithm can be upper bounded by information measures. In most of the rele…
On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models
Xuetong Wu, Mingming Gong, Jonathan H. Manton +2
Recent advancements in unsupervised domain adaptation (UDA) and semi-supervised learning (SSL), particularly incorporating causality, have led to significant methodological improve…
A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task cou…
Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning is a machine learning paradigm where the knowledge from one task is utilized to resolve the problem in a related task. On the one hand, it is conceivable that kno…
Transfer learning to enhance amenorrhea status prediction in cancer and fertility data with missing values
Xuetong Wu, Hadi Akbarzadeh Khorshidi, Uwe Aickelin +2
Collecting sufficient labelled training data for health and medical problems is difficult (Antropova, et al., 2018). Also, missing values are unavoidable in health and medical data…
Imputation techniques on missing values in breast cancer treatment and fertility data
Xuetong Wu, Hadi Akbarzadeh Khorshidi, Uwe Aickelin +2
Clinical decision support using data mining techniques offers more intelligent way to reduce the decision error in the last few years. However, clinical datasets often suffer from…
Information-theoretic analysis for transfer learning
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different distributions (denoted as a…