2.4k citations · 7.9k across the 182 of their papers we have counts for
23 papers · 1 filter
Graduate Employment Prediction with Bias
Teng Guo, Feng Xia, Shihao Zhen +4
The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases c…
Characterizing the Decision Boundary of Deep Neural Networks
Hamid Karimi, Tyler Derr, Jiliang Tang
Deep neural networks and in particular, deep neural classifiers have become an integral part of many modern applications. Despite their practical success, we still have limited kno…
Learning Multi-level Dependencies for Robust Word Recognition
Zhiwei Wang, Hui Liu, Jiliang Tang +3
Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken…
A Double Residual Compression Algorithm for Efficient Distributed Learning
Xiaorui Liu, Yao Li, Jiliang Tang +1
Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms. However, the communication cost of gradient aggregation and model synchron…
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Han Xu, Yao Ma, Haochen Liu +4
Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples has raised conc…
Does Gender Matter? Towards Fairness in Dialogue Systems
Haochen Liu, Jamell Dacon, Wenqi Fan +3
Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-world applications such as computer vision and recommendations. For example, recog…
Say What I Want: Towards the Dark Side of Neural Dialogue Models
Haochen Liu, Tyler Derr, Zitao Liu +1
Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations. However, there…
Automatic Short Answer Grading via Multiway Attention Networks
Tiaoqiao Liu, Wenbiao Ding, Zhiwei Wang +3
Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching profes…
Balance in Signed Bipartite Networks
Tyler Derr, Cassidy Johnson, Yi Chang +1
A large portion of today's big data can be represented as networks. However, not all networks are the same, and in fact, for many that have additional complexities to their structu…
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang +4
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g., e-commerce…
Deep Knowledge Tracing with Side Information
Zhiwei Wang, Xiaoqin Feng, Jiliang Tang +2
Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. Despite its inherent challenges,…
Learning Effective Embeddings From Crowdsourced Labels: An Educational Case Study
Guowei Xu, Wenbiao Ding, Jiliang Tang +3
Learning representation has been proven to be helpful in numerous machine learning tasks. The success of the majority of existing representation learning approaches often requires…
Recommender Systems with Heterogeneous Side Information
Tianqiao Liu, Zhiwei Wang, Jiliang Tang +3
In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items. Such information is typically heterogeneou…
Deep Social Collaborative Filtering
Wenqi Fan, Yao Ma, Dawei Yin +3
Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via t…
R-Transformer: Recurrent Neural Network Enhanced Transformer
Zhiwei Wang, Yao Ma, Zitao Liu +1
Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencie…
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Xiangyu Zhao, Long Xia, Lixin Zou +2
With the recent advances in Reinforcement Learning (RL), there have been tremendous interests in employing RL for recommender systems. However, directly training and evaluating a n…
Attacking Graph Convolutional Networks via Rewiring
Yao Ma, Suhang Wang, Tyler Derr +2
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification. Recent researches show that graph neural…
Deep Adversarial Social Recommendation
Wenqi Fan, Tyler Derr, Yao Ma +3
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social netwo…
Graph Convolutional Networks with EigenPooling
Yao Ma, Suhang Wang, Charu C. Aggarwal +1
Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representa…
Learning Hierarchical Discourse-level Structure for Fake News Detection
Hamid Karimi, Jiliang Tang
On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of information. On…
Deep Adversarial Network Alignment
Tyler Derr, Hamid Karimi, Xiaorui Liu +2
Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure. However, most…
Graph Neural Networks for Social Recommendation
Wenqi Fan, Yao Ma, Qing Li +4
In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph…
Whole-Chain Recommendations
Xiangyu Zhao, Long Xia, Linxin Zou +3
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, us…