1 code implementation • 2 Nov 2023 • Yinghua Yao, Yuangang Pan, Jing Li, Ivor W. Tsang, Xin Yao
Therein, the interested clustering factor and the confounding factor are coarsely considered in the raw feature space, where the correlation between the data and the confounding factor is ideally assumed to be linear for convenient solutions.
1 code implementation • 28 Apr 2023 • Jing Li, Yuangang Pan, Yueming Lyu, Yinghua Yao, Yulei Sui, Ivor W. Tsang
Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate.
no code implementations • 13 Dec 2022 • Peiyao Zhao, Yuangang Pan, Xin Li, Xu Chen, Ivor W. Tsang, Lejian Liao
Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner.
no code implementations • 15 Dec 2021 • Jing Li, Yuangang Pan, Ivor W. Tsang
The prediction uncertainty is typically expressed as the \emph{entropy} computed by the transformed probabilities in output space.
no code implementations • 26 Nov 2021 • Yinghua Yao, Yuangang Pan, Ivor W. Tsang, Xin Yao
In particular, we simultaneously train two modules: a generator that translates an input image to the desired image with smooth subtle changes with respect to the interested attributes; and a ranker that ranks rival preferences consisting of the input image and the desired image.
no code implementations • 29 Sep 2021 • Jing Li, Yuangang Pan, Yueming Lyu, Yinghua Yao, Ivor Tsang
Instead of learning from scratch, fine-tuning a pre-trained model to fit a related target dataset of interest or downstream tasks has been a handy trick to achieve the desired performance.
1 code implementation • 14 Jul 2021 • Yinghua Yao, Yuangang Pan, Ivor W. Tsang, Xin Yao
This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property.
no code implementations • 1 Jan 2021 • Yuangang Pan, Ivor Tsang
We present a new deep neural network architecture, named EDGaM, for deep clustering.
1 code implementation • 15 Sep 2020 • Xu Chen, Ya zhang, Ivor Tsang, Yuangang Pan, Jingchao Su
In this paper, we attempt to learn both features of user preferences in a more principled way.
1 code implementation • 26 Aug 2020 • Xu Chen, Yuangang Pan, Ivor Tsang, Ya zhang
In this paper, we study how to learn node representations against perturbations in GNN.
no code implementations • 24 May 2020 • Yaxin Shi, Yuangang Pan, Donna Xu, Ivor W. Tsang
Multi-view alignment, achieving one-to-one correspondence of multi-view inputs, is critical in many real-world multi-view applications, especially for cross-view data analysis problems.
no code implementations • 30 Mar 2020 • Jing Li, Yuangang Pan, Yulei Sui, Ivor W. Tsang
This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning.
1 code implementation • 15 Aug 2019 • Huiting Hong, Xin Li, Yuangang Pan, Ivor Tsang
Network alignment is a critical task to a wide variety of fields.
no code implementations • 4 Jul 2019 • Yaxin Shi, Yuangang Pan, Donna Xu, Ivor Tsang
Although some works have studied probabilistic interpretation for CCA, these models still require the explicit form of the distributions to achieve a tractable solution for the inference.
1 code implementation • 29 May 2019 • Yuangang Pan, WeiJie Chen, Gang Niu, Ivor W. Tsang, Masashi Sugiyama
Specifically, the properties of our CoarsenRank are summarized as follows: (1) CoarsenRank is designed for mild model misspecification, which assumes there exist the ideal preferences (consistent with model assumption) that locates in a neighborhood of the actual preferences.
no code implementations • ICLR 2019 • Yuangang Pan, Avinash K Singh, Ivor W. Tsang, Chin-Teng Lin
Furthermore, a transition matrix is introduced to characterize the reliability of each channel used in EEG data, which helps in learning brain dynamics preferences only from informative EEG channels.
no code implementations • 27 Sep 2018 • Yaxin Shi, Donna Xu, Yuangang Pan, Ivor Tsang
Based on this objective, we present an implicit probabilistic formulation for CCA, named Implicit CCA (ICCA), which provides a flexible framework to design CCA extensions with implicit distributions.
no code implementations • 3 May 2018 • Yaxin Shi, Donna Xu, Yuangang Pan, Ivor W. Tsang, Shirui Pan
In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges.