Miao Xu

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Pointwise Binary Classification with Pairwise Confidence Comparisons

Ordinary (pointwise) binary classification aims to learn a binary classi...
10/05/2020 ∙ by Lei Feng, et al. ∙ 3 ∙ share

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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...
09/03/2020 ∙ by Zhaoqing Peng, et al. ∙ 8 ∙ share

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Provably Consistent Partial-Label Learning

Partial-label learning (PLL) is a multi-class classification problem, wh...
07/17/2020 ∙ by Lei Feng, et al. ∙ 5 ∙ share

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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...
06/29/2020 ∙ by Xiaotian Hao, et al. ∙ 8 ∙ share

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Progressive Identification of True Labels for Partial-Label Learning

Partial-label learning is one of the important weakly supervised learnin...
02/19/2020 ∙ by Jiaqi Lv, et al. ∙ 0 ∙ share

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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...
01/29/2019 ∙ by Miao Xu, et al. ∙ 0 ∙ share

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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...
09/28/2018 ∙ by Bo Han, et al. ∙ 8 ∙ share

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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...
09/13/2018 ∙ by Takeshi Teshima, et al. ∙ 0 ∙ share

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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 ...
05/23/2018 ∙ by Miao Xu, et al. ∙ 0 ∙ share

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Co-sampling: Training Robust Networks for Extremely Noisy Supervision

Training robust deep networks is challenging under noisy labels. Current...
04/18/2018 ∙ by Bo Han, et al. ∙ 0 ∙ share

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Active Feature Acquisition with Supervised Matrix Completion

Feature missing is a serious problem in many applications, which may lea...
02/15/2018 ∙ by Sheng-Jun Huang, et al. ∙ 0 ∙ share

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Miao Xu

Featured Co-authors

Jun Wang

Masashi Sugiyama

Weinan Zhang

Zhi-Hua Zhou

Gang Niu

Issei Sato

Ivor W. Tsang

Yi Ma

Bo Han

Quanming Yao

Jianye Hao

Pointwise Binary Classification with Pairwise Confidence Comparisons

Learning to Infer User Hidden States for Online Sequential Advertising

Provably Consistent Partial-Label Learning

Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

Progressive Identification of True Labels for Partial-Label Learning

Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels

Clipped Matrix Completion: a Remedy for Ceiling Effects

Matrix Co-completion for Multi-label Classification with Missing Features and Labels

Co-sampling: Training Robust Networks for Extremely Noisy Supervision

Active Feature Acquisition with Supervised Matrix Completion