no code implementations • 14 Dec 2023 • Xiaoqiang Gui, Yueyao Cheng, Xiang-Rong Sheng, Yunfeng Zhao, Guoxian Yu, Shuguang Han, Yuning Jiang, Jian Xu, Bo Zheng
A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving.
no code implementations • 9 Dec 2023 • Dezhi Yang, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Jinglin Zhang
We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data.
no code implementations • 9 Dec 2023 • Jiaxuan Liang, Jun Wang, Guoxian Yu, Shuyin Xia, Guoyin Wang
Unveil, model, and comprehend the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines.
no code implementations • 9 Dec 2023 • Cong Su, Guoxian Yu, Jun Wang, Hui Li, Qingzhong Li, Han Yu
Federated learning (FL) has emerged as a promising collaborative and secure paradigm for training a model from decentralized data without compromising privacy.
no code implementations • 9 Aug 2023 • Yunfeng Zhao, Xu Yan, Xiaoqiang Gui, Shuguang Han, Xiang-Rong Sheng, Guoxian Yu, Jufeng Chen, Zhao Xu, Bo Zheng
Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training.
no code implementations • 28 Nov 2022 • Zijun Gao, Jun Wang, Guoxian Yu, Zhongmin Yan, Carlotta Domeniconi, Jinglin Zhang
LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities.
no code implementations • 22 Nov 2022 • Dezhi Yang, Guoxian Yu, Jun Wang, Zhengtian Wu, Maozu Guo
In this paper, we propose {Reinforcement Causal Structure Learning on Order Graph} (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size.
no code implementations • 7 Nov 2021 • Guangyang Han, Guoxian Yu, Lei Liu, Lizhen Cui, Carlotta Domeniconi, Xiangliang Zhang
First, OSCrowd integrates crowd theme related datasets into a large source domain to facilitate partial transfer learning to approximate the label space inference of these tasks.
no code implementations • 7 Nov 2021 • Yuanlin Yang, Guoxian Yu, Jun Wang, Lei Liu, Carlotta Domeniconi, Maozu Guo
Multi-Instance Multi-Label learning (MIML) models complex objects (bags), each of which is associated with a set of interrelated labels and composed with a set of instances.
no code implementations • 7 Nov 2021 • Runmin Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang
Due to the lack of training samples in the tail classes, MetaCMH first learns direct features from data in different modalities, and then introduces an associative memory module to learn the memory features of samples of the tail classes.
no code implementations • 7 Nov 2021 • Runmin Wang, Guoxian Yu, Lei Liu, Lizhen Cui, Carlotta Domeniconi, Xiangliang Zhang
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search.
no code implementations • 7 Nov 2021 • Guangyang Han, Guoxian Yu, Lizhen Cui, Carlotta Domeniconi, Xiangliang Zhang
Due to the unreliability of Internet workers, it's difficult to complete a crowdsourcing project satisfactorily, especially when the tasks are multiple and the budget is limited.
no code implementations • 2 Jun 2021 • Yunfeng Zhao, Guoxian Yu, Lei Liu, Zhongmin Yan, Lizhen Cui, Carlotta Domeniconi
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label.
no code implementations • 6 Oct 2020 • Yuanlin Yang, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels.
no code implementations • 2 Oct 2020 • Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang
Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering.
no code implementations • 17 Mar 2020 • Tingting Yu, Guoxian Yu, Jun Wang, Maozu Guo
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant.
no code implementations • 24 Dec 2019 • Jingzheng Tu, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
However, they all assume that workers' label quality is stable over time (always at the same level whenever they conduct the tasks).
no code implementations • 26 Nov 2019 • Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta, Xiangliang Zhang
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results.
no code implementations • 17 Nov 2019 • Zhuo Yang, Yufei Han, Guoxian Yu, Qiang Yang, Xiangliang Zhang
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.
no code implementations • 7 Nov 2019 • Jinzheng Tu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Xiangliang Zhang
AMCC accounts for the commonality and individuality of workers, and assumes that workers can be organized into different groups.
no code implementations • 19 Aug 2019 • Xuanwu Liu, Zhao Li, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang
It then defines an objective function to achieve deep feature learning compatible with the composite similarity preserving, category attribute space learning, and hashing coding function learning.
no code implementations • 29 May 2019 • Xuanwu Liu, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang
FlexCMH first introduces a clustering-based matching strategy to explore the local structure of each cluster, and thus to find the potential correspondence between clusters (and samples therein) across modalities.
no code implementations • 14 May 2019 • Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang
To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN).
no code implementations • 13 May 2019 • Shixing Yao, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
It then uses matrix factorization on the individual matrices, along with the shared matrix, to generate diverse clusterings of high-quality.
no code implementations • 13 May 2019 • Yuying Xing, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang, Maozu Guo
To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices.
no code implementations • 11 May 2019 • Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Yazhou Ren, Maozu Guo
Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions.
no code implementations • 10 May 2019 • Xing Wang, Jun Wang, Carlotta Domeniconi, Guoxian Yu, Guo-Qiang Xiao, Maozu Guo
To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering.