
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Fairness in recommendation has attracted increasing attention due to bia...
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Beyond ClassConditional Assumption: A Primary Attempt to Combat InstanceDependent Label Noise
Supervised learning under label noise has seen numerous advances recentl...
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Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
For multiclass classification under classconditional label noise, we p...
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A RotationInvariant Framework for Deep Point Cloud Analysis
Recently, many deep neural networks were designed to process 3D point cl...
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Qvalue Path Decomposition for Deep Multiagent Reinforcement Learning
Recently, deep multiagent reinforcement learning (MARL) has become a hig...
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Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning
In many realworld settings, a team of cooperative agents must learn to ...
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Wasserstein Collaborative Filtering for Item Coldstart Recommendation
The item coldstart problem seriously limits the recommendation performa...
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PMD: A New User Distance for Recommender Systems
Collaborative filtering, a widelyused recommendation technique, predict...
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Spectralbased Graph Convolutional Network for Directed Graphs
Graph convolutional networks(GCNs) have become the most popular approach...
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Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing mach...
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A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR
Noisy labels are ubiquitous in realworld datasets, which poses a challe...
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Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
Graph Neural Networks (GNNs) achieve an impressive performance on struct...
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Understanding Adversarial Behavior of DNNs by Disentangling NonRobust and Robust Components in Performance Metric
The vulnerability to slight input perturbations is a worrying yet intrig...
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Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Value functions are crucial for modelfree Reinforcement Learning (RL) t...
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Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks
In this work, we propose a novel technique to boost training efficiency ...
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Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Noisy labels are ubiquitous in realworld datasets, which poses a challe...
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Online Prediction of Dyadic Data with Heterogeneous Matrix Factorization
Dyadic Data Prediction (DDP) is an important problem in many research ar...
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Blind Image Denoising via Dependent Dirichlet Process Tree
Most existing image denoising approaches assumed the noise to be homogen...
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Guangyong Chen
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