8 citations · 15 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
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…