b'Longbing Cao'

research

∙ 04/26/2023

Bayesian Federated Learning: A Survey

Federated learning (FL) demonstrates its advantages in integrating distr...

0 Longbing Cao, et al. ∙

research

∙ 03/23/2023

ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

Clinic testing plays a critical role in containing infectious diseases s...

0 Yu Dong, et al. ∙

research

∙ 01/27/2023

Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

Multivariate time series (MTS) forecasting has penetrated and benefited ...

0 Hui He, et al. ∙

research

∙ 01/01/2023

eVAE: Evolutionary Variational Autoencoder

The surrogate loss of variational autoencoders (VAEs) poses various chal...

0 Zhangkai Wu, et al. ∙

research

∙ 10/06/2022

Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting

The key problem in multivariate time series (MTS) analysis and forecasti...

0 Kun Yi, et al. ∙

research

∙ 06/29/2022

Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning

Case-based Reasoning (CBR) on high-dimensional and heterogeneous data is...

0 Qi Zhang, et al. ∙

research

∙ 06/19/2022

Label and Distribution-discriminative Dual Representation Learning for Out-of-Distribution Detection

To classify in-distribution samples, deep neural networks learn label-di...

0 Zhilin Zhao, et al. ∙

research

∙ 06/19/2022

Out-of-distribution Detection by Cross-class Vicinity Distribution of In-distribution Data

Deep neural networks only learn to map in-distribution inputs to their c...

0 Zhilin Zhao, et al. ∙

research

∙ 06/19/2022

Supervision Adaptation Balances In-Distribution Generalization and Out-of-Distribution Detection

When there is a discrepancy between in-distribution (ID) samples and out...

0 Zhilin Zhao, et al. ∙

research

∙ 06/19/2022

Gray Learning from Non-IID Data with Out-of-distribution Samples

The quality of the training data annotated by experts cannot be guarante...

0 Zhilin Zhao, et al. ∙

research

∙ 04/04/2022

Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences

Real-life events, behaviors and interactions produce sequential data. An...

0 Wei Wang, et al. ∙

research

∙ 02/12/2022

Coupling Online-Offline Learning for Multi-distributional Data Streams

The distributions of real-life data streams are usually nonstationary, w...

0 Zhilin Zhao, et al. ∙

research

∙ 12/03/2021

Table2Vec: Automated Universal Representation Learning to Encode All-round Data DNA for Benchmarkable and Explainable Enterprise Data Science

Enterprise data typically involves multiple heterogeneous data sources a...

0 Longbing Cao, et al. ∙

research

∙ 08/23/2021

Modeling COVID-19 uncertainties evolving over time and density-dependent social reinforcement and asymptomatic infections

The novel coronavirus disease 2019 (COVID-19) presents unique and unknow...

0 Qing Liu, et al. ∙

research

∙ 08/23/2021

Revealing Distributional Vulnerability of Explicit Discriminators by Implicit Generators

An explicit discriminator trained on observable in-distribution (ID) sam...

0 Zhilin Zhao, et al. ∙

research

∙ 08/19/2021

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making

Automated next-best action recommendation for each customer in a sequent...

0 Longbing Cao, et al. ∙

research

∙ 08/18/2021

DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction

The prediction of express delivery sequence, i.e., modeling and estimati...

0 Siyuan Ren, et al. ∙

research

∙ 07/20/2021

AI in Finance: Challenges, Techniques and Opportunities

AI in finance broadly refers to the applications of AI techniques in fin...

1 Longbing Cao, et al. ∙

research

∙ 06/23/2021

Recurrent Coupled Topic Modeling over Sequential Documents

The abundant sequential documents such as online archival, social media ...

0 Jinjin Guo, et al. ∙

research

∙ 05/13/2021

Graph Learning based Recommender Systems: A Review

Recent years have witnessed the fast development of the emerging topic o...

90 Shoujin Wang, et al. ∙

research

∙ 04/16/2021

COVID-19 Modeling: A Review

To tackle the COVID-19 pandemic, massive efforts have been made in model...

0 Longbing Cao, et al. ∙

research

∙ 03/21/2021

Homophily Outlier Detection in Non-IID Categorical Data

Most of existing outlier detection methods assume that the outlier facto...

22 Guansong Pang, et al. ∙

research

∙ 09/15/2020

Deep Reinforcement Learning for Unknown Anomaly Detection

We address a critical yet largely unsolved anomaly detection problem, in...

40 Guansong Pang, et al. ∙

research

∙ 08/09/2020

Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences

Scientific article recommender systems are playing an increasingly impor...

0 Feng Xia, et al. ∙

research

∙ 07/21/2020

Unsupervised Heterogeneous Coupling Learning for Categorical Representation

Complex categorical data is often hierarchically coupled with heterogene...

0 Chengzhang Zhu, et al. ∙

research

∙ 07/10/2020

AI in FinTech: A Research Agenda

Smart FinTech has emerged as a new area that synthesizes and transforms ...

0 Longbing Cao, et al. ∙

research

∙ 07/06/2020

Deep Learning for Anomaly Detection: A Review

Anomaly detection, a.k.a. outlier detection, has been a lasting yet acti...

59 Guansong Pang, et al. ∙

research

∙ 07/01/2020

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

While recommendation plays an increasingly critical role in our living, ...

0 Longbing Cao, et al. ∙

research

∙ 07/01/2020

Coupling Learning of Complex Interactions

Complex applications such as big data analytics involve different forms ...

0 Longbing Cao, et al. ∙

research

∙ 07/01/2020

Data Science: A Comprehensive Overview

The twenty-first century has ushered in the age of big data and data eco...

0 Longbing Cao, et al. ∙

research

∙ 06/28/2020

Data Science: Nature and Pitfalls

Data science is creating very exciting trends as well as significant con...

0 Longbing Cao, et al. ∙

research

∙ 06/28/2020

Data Science: Challenges and Directions

While data science has emerged as a contentious new scientific field, en...

0 Longbing Cao, et al. ∙

research

∙ 05/30/2020

Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation

A transaction-based recommender system (TBRS) aims to predict the next i...

0 Shoujin Wang, et al. ∙

research

∙ 04/22/2020

Graph Learning Approaches to Recommender Systems: A Review

Recent years have witnessed the fast development of the emerging topic o...

0 Shoujin Wang, et al. ∙

research

∙ 12/28/2019

Sequential Recommender Systems: Challenges, Progress and Prospects

The emerging topic of sequential recommender systems has attracted incre...

0 Shoujin Wang, et al. ∙

research

∙ 07/01/2019

FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control

In recent years significant progress has been made in dealing with chall...

5 Longxiang Shi, et al. ∙

research

∙ 05/25/2019

Multi-view Information-theoretic Co-clustering for Co-occurrence Data

Multi-view clustering has received much attention recently. Most of the ...

0 Peng Xu, et al. ∙

research

∙ 05/17/2019

TBQ(σ): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning

Off-policy reinforcement learning with eligibility traces is challenging...

0 Longxiang Shi, et al. ∙

research

∙ 02/13/2019

A Survey on Session-based Recommender Systems

Session-based recommender systems (SBRS) are an emerging topic in the re...

0 Shoujin Wang, et al. ∙

research

∙ 06/13/2018

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

Learning expressive low-dimensional representations of ultrahigh-dimensi...

0 Guansong Pang, et al. ∙

research

∙ 10/06/2013

Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network

Effectively modelling hidden structures in a network is very practical b...

0 Xuhui Fan, et al. ∙

research

∙ 06/13/2013

Non-parametric Power-law Data Clustering

It has always been a great challenge for clustering algorithms to automa...

0 Xuhui Fan, et al. ∙

research

∙ 06/13/2013

A Convergence Theorem for the Graph Shift-type Algorithms

Graph Shift (GS) algorithms are recently focused as a promising approach...

0 Xuhui Fan, et al. ∙

research

∙ 06/13/2013

Dynamic Infinite Mixed-Membership Stochastic Blockmodel

Directional and pairwise measurements are often used to model inter-rela...

0 Xuhui Fan, et al. ∙

research

∙ 06/12/2013

Copula Mixed-Membership Stochastic Blockmodel for Intra-Subgroup Correlations

The Mixed-Membership Stochastic Blockmodel (MMSB) is a popular framework...

0 Xuhui Fan, et al. ∙

research

∙ 05/24/2013

Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models

This paper proposes a generative model, the latent Dirichlet hidden Mark...

0 Yin Song, et al. ∙

Longbing Cao

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