9 citations · 38 across the 27 of their papers we have counts for
31 papers
Federated Causality Learning with Explainable Adaptive Optimization
Dezhi Yang, Xintong He, Jun Wang +3
Discovering the causality from observational data is a crucial task in various scientific domains. With increasing awareness of privacy, data are not allowed to be exposed, and it…
Long-tail Cross Modal Hashing
Zijun Gao, Jun Wang, Guoxian Yu +3
Existing Cross Modal Hashing (CMH) methods are mainly designed for balanced data, while imbalanced data with long-tail distribution is more general in real-world. Several long-tail…
Hub-VAE: Unsupervised Hub-based Regularization of Variational Autoencoders
Priya Mani, Carlotta Domeniconi
Exemplar-based methods rely on informative data points or prototypes to guide the optimization of learning algorithms. Such data facilitate interpretable model design and predictio…
Incentive-boosted Federated Crowdsourcing
Xiangping Kang, Guoxian Yu, Jun Wang +3
Crowdsourcing is a favorable computing paradigm for processing computer-hard tasks by harnessing human intelligence. However, generic crowdsourcing systems may lead to privacy-leak…
MetaMIML: Meta Multi-Instance Multi-Label Learning
Yuanlin Yang, Guoxian Yu, Jun Wang +3
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. Curren…
Meta Cross-Modal Hashing on Long-Tailed Data
Runmin Wang, Guoxian Yu, Carlotta Domeniconi +1
Due to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor s…
Cross-modal Zero-shot Hashing by Label Attributes Embedding
Runmin Wang, Guoxian Yu, Lei Liu +3
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and test…
Open-Set Crowdsourcing using Multiple-Source Transfer Learning
Guangyang Han, Guoxian Yu, Lei Liu +3
We raise and define a new crowdsourcing scenario, open set crowdsourcing, where we only know the general theme of an unfamiliar crowdsourcing project, and we don't know its label s…
Crowdsourcing with Meta-Workers: A New Way to Save the Budget
Guangyang Han, Guoxian Yu, Lizhen Cui +2
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. R…
Group-Node Attention for Community Evolution Prediction
Matt Revelle, Carlotta Domeniconi, Ben Gelman
Communities in social networks evolve over time as people enter and leave the network and their activity behaviors shift. The task of predicting structural changes in communities o…
Few-Shot Partial-Label Learning
Yunfeng Zhao, Guoxian Yu, Lei Liu +3
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…
Unsupervised Selective Manifold Regularized Matrix Factorization
Priya Mani, Carlotta Domeniconi, Igor Griva
Manifold regularization methods for matrix factorization rely on the cluster assumption, whereby the neighborhood structure of data in the input space is preserved in the factoriza…
Effects of Model Misspecification on Bayesian Bandits: Case Studies in UX Optimization
Mack Sweeney, Matthew van Adelsberg, Kathryn Laskey +1
Bayesian bandits using Thompson Sampling have seen increasing success in recent years. Yet existing value models (of rewards) are misspecified on many real-world problem. We demons…
Multi-typed Objects Multi-view Multi-instance Multi-label Learning
Yuanlin Yang, Guoxian Yu, Jun Wang +2
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 wit…
Deep Incomplete Multi-View Multiple Clusterings
Shaowei Wei, Jun Wang, Guoxian Yu +2
Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering. Most previous works search for only one optimal clustering based on th…
Attention-Aware Answers of the Crowd
Jingzheng Tu, Guoxian Yu, Jun Wang +2
Crowdsourcing is a relatively economic and efficient solution to collect annotations from the crowd through online platforms. Answers collected from workers with different expertis…
Multi-View Multiple Clusterings using Deep Matrix Factorization
Shaowei Wei, Jun Wang, Guoxian Yu +2
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can on…
Active Multi-Label Crowd Consensus
Jinzheng Tu, Guoxian Yu, Carlotta Domeniconi +2
Crowdsourcing is an economic and efficient strategy aimed at collecting annotations of data through an online platform. Crowd workers with different expertise are paid for their se…
Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
Hamed Sarvari, Carlotta Domeniconi, Bardh Prenkaj +1
Autoencoders, as a dimensionality reduction technique, have been recently applied to outlier detection. However, neural networks are known to be vulnerable to overfitting, and ther…
Estimator Vectors: OOV Word Embeddings based on Subword and Context Clue Estimates
Raj Patel, Carlotta Domeniconi
Semantic representations of words have been successfully extracted from unlabeled corpuses using neural network models like word2vec. These representations are generally high quali…
Cross-modal Zero-shot Hashing
Xuanwu Liu, Zhao Li, Jun Wang +3
Hashing has been widely studied for big data retrieval due to its low storage cost and fast query speed. Zero-shot hashing (ZSH) aims to learn a hashing model that is trained using…
Weakly-paired Cross-Modal Hashing
Xuanwu Liu, Jun Wang, Guoxian Yu +2
Hashing has been widely adopted for large-scale data retrieval in many domains, due to its low storage cost and high retrieval speed. Existing cross-modal hashing methods optimisti…
ActiveHNE: Active Heterogeneous Network Embedding
Xia Chen, Guoxian Yu, Jun Wang +3
Heterogeneous network embedding (HNE) is a challenging task due to the diverse node types and/or diverse relationships between nodes. Existing HNE methods are typically unsupervise…
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Yuying Xing, Guoxian Yu, Carlotta Domeniconi +3
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple…