2.4k citations · 7.9k across the 182 of their papers we have counts for
18 papers · 1 filter
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Yiqi Wang, Chaozhuo Li, Zheng Liu +5
Graph neural networks (GNNs) have been widely applied in the recommendation tasks and have obtained very appealing performance. However, most GNN-based recommendation methods suffe…
Toward Annotator Group Bias in Crowdsourcing
Haochen Liu, Joseph Thekinen, Sinem Mollaoglu +5
Crowdsourcing has emerged as a popular approach for collecting annotated data to train supervised machine learning models. However, annotator bias can lead to defective annotations…
Graph Condensation for Graph Neural Networks
Wei Jin, Lingxiao Zhao, Shichang Zhang +3
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, w…
What Truly Matters? Using Linguistic Cues for Analyzing the #BlackLivesMatter Movement and its Counter Protests: 2013 to 2020
Jamell Dacon, Jiliang Tang
Since the fatal shooting of 17-year old Black teenager Trayvon Martin in February 2012 by a White neighborhood watchman, George Zimmerman in Sanford, Florida, there has been a sign…
Trustworthy AI: A Computational Perspective
Haochen Liu, Yiqi Wang, Wenqi Fan +6
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human soci…
Decentralized Composite Optimization with Compression
Yao Li, Xiaorui Liu, Jiliang Tang +2
Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottlenec…
Graph Trend Filtering Networks for Recommendations
Wenqi Fan, Xiaorui Liu, Wei Jin +3
Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict ho…
Jointly Attacking Graph Neural Network and its Explanations
Wenqi Fan, Wei Jin, Xiaorui Liu +7
Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adve…
Imbalanced Adversarial Training with Reweighting
Wentao Wang, Han Xu, Xiaorui Liu +3
Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks. However, almost all existing studies abou…
Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models
Qiongqiong Liu, Tianqiao Liu, Jiafu Zhao +6
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options. SC questions are widely…
Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models
Yang Hao, Hang Li, Wenbiao Ding +4
In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective st…
Elastic Graph Neural Networks
Xiaorui Liu, Wei Jin, Yao Ma +5
While many existing graph neural networks (GNNs) have been proven to perform -based graph smoothing that enforces smoothness globally, in this work we aim to further enhanc…
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao, Haochen Liu, Wenqi Fan +3
Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could l…
Towards the Memorization Effect of Neural Networks in Adversarial Training
Han Xu, Xiaorui Liu, Wentao Wang +5
Recent studies suggest that ``memorization'' is one important factor for overparameterized deep neural networks (DNNs) to achieve optimal performance. Specifically, the perfectly f…
Is Homophily a Necessity for Graph Neural Networks?
Yao Ma, Xiaorui Liu, Neil Shah +1
Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node cla…
Automated Self-Supervised Learning for Graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao +3
Graph self-supervised learning has gained increasing attention due to its capacity to learn expressive node representations. Many pretext tasks, or loss functions have been designe…
Graph Feature Gating Networks
Wei Jin, Xiaorui Liu, Yao Ma +3
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs. Most GNNs follow a message-passing scheme where…
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification
Haochen Liu, Wei Jin, Hamid Karimi +2
It is evident that deep text classification models trained on human data could be biased. In particular, they produce biased outcomes for texts that explicitly include identity ter…