Miao Xu

is this you? claim profile

0 followers

Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising

In e-commerce advertising, the ad platform usually relies on auction mec...
12/05/2020 ∙ by Zhilin Zhang, et al. ∙ 0 ∙ share

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it

Miao Xu

Featured Co-authors

Jun Wang

Masashi Sugiyama

Weinan Zhang

Gang Niu

Zhi-Hua Zhou

Issei Sato

Bo Han

Ivor W. Tsang

Yi Ma

Quanming Yao

Fan Wu

Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising

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