
NonIID Graph Neural Networks
Graph classification is an important task on graphstructured data with ...
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Graph Structure Learning for Robust Graph Neural Networks
Graph Neural Networks (GNNs) are powerful tools in representation learni...
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Attacking Blackbox Recommendations via Copying Crossdomain User Profiles
Recently, recommender systems that aim to suggest personalized lists of ...
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Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Deep neural networks (DNN) have achieved unprecedented success in numero...
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Deep Social Collaborative Filtering
Recommender systems are crucial to alleviate the information overload pr...
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RTransformer: Recurrent Neural Network Enhanced Transformer
Recurrent Neural Networks have long been the dominating choice for seque...
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Attacking Graph Convolutional Networks via Rewiring
Graph Neural Networks (GNNs) have boosted the performance of many graph ...
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Deep Adversarial Social Recommendation
Recent years have witnessed rapid developments on social recommendation ...
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Graph Convolutional Networks with EigenPooling
Graph neural networks, which generalize deep neural network models to gr...
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Gradient Descent for Sparse RankOne Matrix Completion for CrowdSourced Aggregation of Sparsely Interacting Workers
We consider worker skill estimation for the singlecoin DawidSkene crow...
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Graph Neural Networks for Social Recommendation
In recent years, Graph Neural Networks (GNNs), which can naturally integ...
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Dynamic Graph Neural Networks
Graphs, which describe pairwise relations between objects, are essential...
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Automata Guided Reinforcement Learning With Demonstrations
Tasks with complex temporal structures and long horizons pose a challeng...
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Linked Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have been proven to be effective in mod...
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Automata Guided Hierarchical Reinforcement Learning for Zeroshot Skill Composition
An obstacle that prevents the wide adoption of (deep) reinforcement lear...
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A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks
Reward engineering is an important aspect of reinforcement learning. Whe...
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Theoretical Comparisons of PositiveUnlabeled Learning against PositiveNegative Learning
In PU learning, a binary classifier is trained from positive (P) and unl...
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Yao Ma
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