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Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mec...
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Pointwise Binary Classification with Pairwise Confidence Comparisons
Ordinary (pointwise) binary classification aims to learn a binary classi...
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Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great...
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Provably Consistent Partial-Label Learning
Partial-label learning (PLL) is a multi-class classification problem, wh...
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Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their tar...
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Progressive Identification of True Labels for Partial-Label Learning
Partial-label learning is one of the important weakly supervised learnin...
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Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative
In the early history of positive-unlabeled (PU) learning, the sample sel...
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Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
It is challenging to train deep neural networks robustly on the industri...
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Clipped Matrix Completion: a Remedy for Ceiling Effects
We consider the recovery of a low-rank matrix from its clipped observati...
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Matrix Co-completion for Multi-label Classification with Missing Features and Labels
We consider a challenging multi-label classification problem where both ...
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Co-sampling: Training Robust Networks for Extremely Noisy Supervision
Training robust deep networks is challenging under noisy labels. Current...
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Active Feature Acquisition with Supervised Matrix Completion
Feature missing is a serious problem in many applications, which may lea...
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