9 citations · 38 across the 27 of their papers we have counts for
23 papers · 1 filter
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…
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…
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…
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…
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…
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…
Multi-View Multiple Clustering
Shixing Yao, Guoxian Yu, Jun Wang +2
Multiple clustering aims at exploring alternative clusterings to organize the data into meaningful groups from different perspectives. Existing multiple clustering algorithms are d…
Ranking-based Deep Cross-modal Hashing
Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi +3
Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are…
Multiple Independent Subspace Clusterings
Xing Wang, Jun Wang, Carlotta Domeniconi +3
Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it's still a challenge for users to analyze and understand the dis…
Graph-based Selective Outlier Ensembles
Hamed Sarvari, Carlotta Domeniconi, Giovanni Stilo
An ensemble technique is characterized by the mechanism that generates the components and by the mechanism that combines them. A common way to achieve the consensus is to enable ea…
Theoretical and Empirical Analysis of a Parallel Boosting Algorithm
Uday Kamath, Carlotta Domeniconi, Kenneth De Jong
Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to…