Gang Niu

Gang Niu's AcademicInfluence.com Rankings

Gang Niu

Computer Science

#11097

World Rank

#11723

Historical Rank

Algorithms

#454

World Rank

#460

Historical Rank

Machine Learning

#5073

World Rank

#5136

Historical Rank

Artificial Intelligence

#5471

World Rank

#5546

Historical Rank

computer-science Degrees

Download Badge

Computer Science

Gang Niu's Degrees

Bachelors Computer Science Peking University

Similar Degrees You Can Earn

Why Is Gang Niu Influential?

(Suggest an Edit or Addition)

(See a Problem?)

Gang Niu's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Co-teaching: Robust training of deep neural networks with extremely noisy labels (2018) (1177)
How does Disagreement Help Generalization against Label Corruption? (2019) (427)
Positive-Unlabeled Learning with Non-Negative Risk Estimator (2017) (300)
Analysis of Learning from Positive and Unlabeled Data (2014) (283)
Convex Formulation for Learning from Positive and Unlabeled Data (2015) (243)
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (2020) (228)
Are Anchor Points Really Indispensable in Label-Noise Learning? (2019) (205)
Does Distributionally Robust Supervised Learning Give Robust Classifiers? (2016) (186)
Masking: A New Perspective of Noisy Supervision (2018) (179)
Geometry-aware Instance-reweighted Adversarial Training (2020) (136)
Class-prior estimation for learning from positive and unlabeled data (2016) (129)
Parts-dependent Label Noise: Towards Instance-dependent Label Noise (2020) (121)
Analysis and Improvement of Policy Gradient Estimation (2011) (119)
Learning from Complementary Labels (2017) (106)
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning (2020) (102)
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning (2016) (94)
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust (2018) (84)
Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization (2012) (84)
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data (2016) (79)
Progressive Identification of True Labels for Partial-Label Learning (2020) (78)
A Survey of Label-noise Representation Learning: Past, Present and Future (2020) (78)
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data (2018) (65)
Do We Need Zero Training Loss After Achieving Zero Training Error? (2020) (65)
Complementary-Label Learning for Arbitrary Losses and Models (2018) (64)
Classification from Pairwise Similarity and Unlabeled Data (2018) (61)
Searching to Exploit Memorization Effect in Learning with Noisy Labels (2020) (56)
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations (2021) (56)
Rethinking Importance Weighting for Deep Learning under Distribution Shift (2020) (55)
Learning with Multiple Complementary Labels (2020) (55)
Provably Consistent Partial-Label Learning (2020) (52)
Confidence Scores Make Instance-dependent Label-noise Learning Possible (2019) (50)
Classification from Positive, Unlabeled and Biased Negative Data (2018) (48)
Provably End-to-end Label-Noise Learning without Anchor Points (2021) (47)
Understanding and Improving Early Stopping for Learning with Noisy Labels (2021) (45)
Semi-supervised AUC optimization based on positive-unlabeled learning (2017) (43)
Information-Maximization Clustering Based on Squared-Loss Mutual Information (2011) (42)
PiCO: Contrastive Label Disambiguation for Partial Label Learning (2022) (40)
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning (2013) (38)
Active Feature Acquisition with Supervised Matrix Completion (2018) (36)
Binary Classification from Positive-Confidence Data (2017) (34)
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels (2020) (31)
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach (2019) (31)
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels (2021) (31)
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization (2021) (29)
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels (2020) (26)
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks (2020) (25)
How Does Disagreement Benefit Co-teaching? (2019) (25)
Adversarial Robustness through the Lens of Causality (2022) (22)
Estimating Instance-dependent Label-noise Transition Matrix using DNNs (2021) (21)
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation (2019) (21)
Instance-dependent Label-noise Learning under a Structural Causal Model (2021) (20)
Transfer Learning via Multi-View Principal Component Analysis (2011) (19)
Computationally efficient sufficient dimension reduction via squared-loss mutual information (2011) (18)
Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances (2013) (18)
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels (2018) (17)
Class2Simi: A New Perspective on Learning with Label Noise (2020) (16)
Large-Margin Contrastive Learning with Distance Polarization Regularizer (2021) (16)
Non-Gaussian Component Analysis with Log-Density Gradient Estimation (2015) (16)
Probabilistic Margins for Instance Reweighting in Adversarial Training (2021) (15)
Semi-supervised information-maximization clustering (2013) (14)
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection (2021) (14)
Understanding the Interaction of Adversarial Training with Noisy Labels (2021) (14)
Maximum Mean Discrepancy is Aware of Adversarial Attacks (2020) (14)
Reliable Adversarial Distillation with Unreliable Teachers (2021) (13)
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data (2018) (13)
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients (2022) (13)
Co-sampling: Training Robust Networks for Extremely Noisy Supervision (2018) (12)
Matrix Co-completion for Multi-label Classification with Missing Features and Labels (2018) (12)
Understanding (Generalized) Label Smoothing when Learning with Noisy Labels (2021) (12)
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation (2022) (12)
Maximum volume clustering: a new discriminative clustering approach (2013) (11)
Direct Density Derivative Estimation (2016) (11)
Learning Diverse-Structured Networks for Adversarial Robustness (2021) (10)
To Smooth or Not? When Label Smoothing Meets Noisy Labels (2021) (10)
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative (2019) (10)
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model (2021) (10)
Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation (2015) (10)
Exploiting Class Activation Value for Partial-Label Learning (2022) (10)
Searching to Exploit Memorization Effect in Learning from Corrupted Labels (2019) (9)
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data (2021) (9)
Guided Interpolation for Adversarial Training (2021) (9)
Uncoupled Regression from Pairwise Comparison Data (2019) (8)
On the Robustness of Average Losses for Partial-Label Learning (2021) (8)
Multi-Class Classification from Noisy-Similarity-Labeled Data (2020) (8)
CausalAdv: Adversarial Robustness through the Lens of Causality (2021) (8)
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning (2020) (8)
Pumpout: A Meta Approach to Robust Deep Learning with Noisy Labels (2018) (7)
Instance Correction for Learning with Open-set Noisy Labels (2021) (7)
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision (2019) (7)
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling (2018) (7)
Towards Mixture Proportion Estimation without Irreducibility (2020) (7)
Multiple-Instance Learning from Similar and Dissimilar Bags (2021) (6)
Pointwise Binary Classification with Pairwise Confidence Comparisons (2020) (6)
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification (2021) (5)
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning (2020) (5)
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning (2019) (5)
Where is the Bottleneck of Adversarial Learning with Unlabeled Data? (2019) (5)
Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning (2016) (5)
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios (2017) (5)
Maximum Volume Clustering (2011) (4)
Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities (2015) (4)
Bayesian Maximum Margin Clustering (2010) (4)
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels (2021) (4)
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network (2021) (4)
On the Effectiveness of Adversarial Training against Backdoor Attacks (2022) (4)
Whitening-Free Least-Squares Non-Gaussian Component Analysis (2016) (3)
Learning from Similarity-Confidence Data (2021) (3)
SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity (2011) (3)
Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure (2020) (2)
Logit Clipping for Robust Learning against Label Noise (2022) (2)
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? (2021) (2)
Local Reweighting for Adversarial Training (2021) (2)
Butterfly: Robust One-step Approach towards Wildly-unsupervised Domain Adaptation (2019) (2)
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning (2022) (2)
Class-prior estimation for learning from positive and unlabeled data (2016) (2)
Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data (2016) (2)
Semi-Supervised Classification based on Positive-Unlabeled Classification (2017) (1)
Compact Margin Machine (2010) (1)
Information-Theoretic Representation Learning for Positive-Unlabeled Classification (2017) (1)
Transductive Learning with Multi-class Volume Approximation (2014) (1)
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack (2022) (1)
Fast and Robust Rank Aggregation against Model Misspecification (2019) (1)
Fairness Improves Learning from Noisily Labeled Long-Tailed Data (2023) (1)
Class-Wise Denoising for Robust Learning Under Label Noise (2022) (1)
Sufficient Component Analysis for Supervised Dimension Reduction (2011) (1)
Multi-Class Classification from Single-Class Data with Confidences (2021) (1)
GEOMETRY-AWARE INSTANCE-REWEIGHTED ADVER- (2020) (1)
Revisiting Distributionally Robust Supervised Learning in Classification (2016) (1)
Demystifying Assumptions in Learning to Discover Novel Classes (2021) (0)
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses (2022) (0)
F EDERATED L EARNING FROM O NLY U NLABELED D ATA WITH C LASS -C ONDITIONAL -S HARING C LIENTS (2022) (0)
Learning from Noisy Pairwise Similarity and Unlabeled Data (2022) (0)
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning (2023) (0)
S2GA: Robust Deep Learning with Noisy Labels without Early Stopping (2020) (0)
Suffcient Component Analysis (2011) (0)
FedMT: Federated Learning with Mixed-type Labels (2022) (0)
Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs (2019) (0)
Semi-supervised AUC optimization based on positive-unlabeled learning (2017) (0)
Learning and Mining with Noisy Labels (2022) (0)
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks (2022) (0)
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning (2022) (0)
Rough Margin Based Core Vector Machine (2010) (0)
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks (2023) (0)
Learning Contrastive Embedding in Low-Dimensional Space (2022) (0)
Co-teaching + : Towards Training of Robust Deep Networks with Noisy Labels (2019) (0)
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification (2022) (0)
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach (2021) (0)
Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data (2017) (0)
Wildly Unsupervised Domain Adaptation and Its Powerful and Efficient Solution (2019) (0)

This paper list is powered by the following services:

Gang Niu's Academic­Influence.com Rankings

Gang Niu's Degrees

Similar Degrees You Can Earn

Why Is Gang Niu Influential?

Gang Niu's Published Works

Published Works

Gang Niu's AcademicInfluence.com Rankings