Search Results for author: Miao Xu

Found 24 papers, 5 papers with code

A Boosting Algorithm for Positive-Unlabeled Learning

no code implementations19 May 2022 Yawen Zhao, Mingzhe Zhang, Chenhao Zhang, Tony Chen, Nan Ye, Miao Xu

Considering that in some scenarios when neural networks cannot perform as good as boosting algorithms even with fully-supervised data, we propose a novel boosting algorithm for PU learning: Ada-PU, which compares against neural networks.

Single-Image 3D Face Reconstruction under Perspective Projection

no code implementations9 May 2022 Yueying Kao, Bowen Pan, Miao Xu, Jiangjing Lyu, Xiangyu Zhu, Yuanzhang Chang, Xiaobo Li, Zhen Lei, Zixiong Qin

In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process.

3D Face Reconstruction

Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent

no code implementations17 Dec 2021 Guanhua Ye, Hongzhi Yin, Tong Chen, Miao Xu, Quoc Viet Hung Nguyen, Jiangning Song

Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.

Fairness Medical Diagnosis

Active Refinement for Multi-Label Learning: A Pseudo-Label Approach

no code implementations29 Sep 2021 Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts.

Active Learning Multi-Label Learning

On the Robustness of Average Losses for Partial-Label Learning

no code implementations11 Jun 2021 Jiaqi Lv, Lei Feng, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama

Partial-label (PL) learning is a typical weakly supervised classification problem, where a PL of an instance is a set of candidate labels such that a fixed but unknown candidate is the true label.

Partial Label Learning Weakly Supervised Classification

Particle-hole asymmetric superconducting coherence peaks in overdoped cuprates

no code implementations10 Mar 2021 Changwei Zou, Zhenqi Hao, Xiangyu Luo, Shusen Ye, Qiang Gao, Xintong Li, Miao Xu, Peng Cai, Chengtian Lin, Xingjiang Zhou, Dung-Hai Lee, Yayu Wang

To elucidate the superconductor to metal transition at the end of superconducting dome, the overdoped regime has stepped onto the center stage of cuprate research recently.


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

no code implementations5 Dec 2020 Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang, Miao Xu, Junwei Pan, Chuan Yu, Fan Wu, Jian Xu, Kun Gai

In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.

Pointwise Binary Classification with Pairwise Confidence Comparisons

no code implementations5 Oct 2020 Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama

To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed.

Classification General Classification

Learning to Infer User Hidden States for Online Sequential Advertising

no code implementations3 Sep 2020 Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Wei-Nan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.


Provably Consistent Partial-Label Learning

no code implementations NeurIPS 2020 Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama

Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels.

Multi-class Classification Partial Label Learning

Progressive Identification of True Labels for Partial-Label Learning

1 code implementation ICML 2020 Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label.

Partial Label Learning Stochastic Optimization

A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision

no code implementations ICLR Workshop LLD 2019 Cheng-Yu Hsieh, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

To address the need, we propose a special weakly supervised MLL problem that not only focuses on the situation of limited fine-grained supervision but also leverages the hierarchical relationship between the coarse concepts and the fine-grained ones.

Meta-Learning Multi-Label Learning

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

no code implementations29 Jan 2019 Miao Xu, Bingcong Li, Gang Niu, Bo Han, Masashi Sugiyama

May there be a new sample selection method that can outperform the latest importance reweighting method in the deep learning age?

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

1 code implementation ICML 2020 Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama

Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end.

Learning with noisy labels

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

no code implementations27 Sep 2018 Bo Han, Gang Niu, Jiangchao Yao, Xingrui Yu, Miao Xu, Ivor Tsang, Masashi Sugiyama

To handle these issues, by using the memorization effects of deep neural networks, we may train deep neural networks on the whole dataset only the first few iterations.

Clipped Matrix Completion: A Remedy for Ceiling Effects

no code implementations13 Sep 2018 Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama

On the other hand, matrix completion (MC) methods can recover a low-rank matrix from various information deficits by using the principle of low-rank completion.

Matrix Completion Recommendation Systems

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

no code implementations23 May 2018 Miao Xu, Gang Niu, Bo Han, Ivor W. Tsang, Zhi-Hua Zhou, Masashi Sugiyama

We consider a challenging multi-label classification problem where both feature matrix $\X$ and label matrix $\Y$ have missing entries.

General Classification Matrix Completion +1

Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

5 code implementations NeurIPS 2018 Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama

Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training.

Learning with noisy labels

Active Feature Acquisition with Supervised Matrix Completion

no code implementations15 Feb 2018 Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen

Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance.

Matrix Completion

CUR Algorithm for Partially Observed Matrices

no code implementations4 Nov 2014 Miao Xu, Rong Jin, Zhi-Hua Zhou

In particular, the proposed algorithm computes the low rank approximation of the target matrix based on (i) the randomly sampled rows and columns, and (ii) a subset of observed entries that are randomly sampled from the matrix.

Matrix Completion

Speedup Matrix Completion with Side Information: Application to Multi-Label Learning

no code implementations NeurIPS 2013 Miao Xu, Rong Jin, Zhi-Hua Zhou

In standard matrix completion theory, it is required to have at least $O(n\ln^2 n)$ observed entries to perfectly recover a low-rank matrix $M$ of size $n\times n$, leading to a large number of observations when $n$ is large.

Matrix Completion Multi-Label Learning

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