Bo Han

Orcid: 0000-0002-9226-0461

Affiliations:

Hong Kong Baptist University, Department of Computer Science, Hong Kong
University of Technology Sydney, Centre for Artificial Intelligence, NSW, Australia

According to our database¹, Bo Han authored at least 196 papers between 2016 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

Regularly Truncated M-Estimators for Learning With Noisy Labels.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., May, 2024

Server-Client Collaborative Distillation for Federated Reinforcement Learning.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, January, 2024

Few-Shot Adversarial Prompt Learning on Vision-Language Models.

[BibT_eX]

[DOI]

CoRR, 2024

Tackling Noisy Labels with Network Parameter Additive Decomposition.

[BibT_eX]

[DOI]

CoRR, 2024

Do CLIPs Always Generalize Better than ImageNet Models?

[BibT_eX]

[DOI]

CoRR, 2024

NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation.

[BibT_eX]

[DOI]

CoRR, 2024

Mitigating Label Noise on Graph via Topological Sample Selection.

[BibT_eX]

[DOI]

CoRR, 2024

Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy.

[BibT_eX]

[DOI]

CoRR, 2024

Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting.

[BibT_eX]

[DOI]

CoRR, 2024

Robust Training of Federated Models with Extremely Label Deficiency.

[BibT_eX]

[DOI]

CoRR, 2024

FedImpro: Measuring and Improving Client Update in Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Discovery of the Hidden World with Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2024

Enhancing Neural Subset Selection: Integrating Background Information into Set Representations.

[BibT_eX]

[DOI]

CoRR, 2024

Enhancing Evolving Domain Generalization through Dynamic Latent Representations.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Deep Learning From Multiple Noisy Annotators as A Union.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., December, 2023

A Parametrical Model for Instance-Dependent Label Noise.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., December, 2023

Latent Class-Conditional Noise Model.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., August, 2023

GRACE: A General Graph Convolution Framework for Attributed Graph Clustering.

[BibT_eX]

[DOI]

Barakeel Fanseu Kamhoua

ACM Trans. Knowl. Discov. Data, April, 2023

Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., March, 2023

Class-Wise Denoising for Robust Learning Under Label Noise.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., March, 2023

KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Are All Unseen Data Out-of-Distribution?

[BibT_eX]

[DOI]

CoRR, 2023

Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes.

[BibT_eX]

[DOI]

CoRR, 2023

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources.

[BibT_eX]

[DOI]

CoRR, 2023

DeepInception: Hypnotize Large Language Model to Be Jailbreaker.

[BibT_eX]

[DOI]

CoRR, 2023

Learning to Augment Distributions for Out-of-Distribution Detection.

[BibT_eX]

[DOI]

CoRR, 2023

Combating Bilateral Edge Noise for Robust Link Prediction.

[BibT_eX]

[DOI]

CoRR, 2023

Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

[BibT_eX]

[DOI]

CoRR, 2023

InstanT: Semi-supervised Learning with Instance-dependent Thresholds.

[BibT_eX]

[DOI]

CoRR, 2023

Combating Representation Learning Disparity with Geometric Harmonization.

[BibT_eX]

[DOI]

CoRR, 2023

Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization.

[BibT_eX]

[DOI]

CoRR, 2023

Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis.

[BibT_eX]

[DOI]

CoRR, 2023

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation.

[BibT_eX]

[DOI]

CoRR, 2023

On the Over-Memorization During Natural, Robust and Catastrophic Overfitting.

[BibT_eX]

[DOI]

CoRR, 2023

Towards out-of-distribution generalizable predictions of chemical kinetics properties.

[BibT_eX]

[DOI]

CoRR, 2023

On the Onset of Robust Overfitting in Adversarial Training.

[BibT_eX]

[DOI]

CoRR, 2023

Regularly Truncated M-estimators for Learning with Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2023

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation.

[BibT_eX]

[DOI]

CoRR, 2023

Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2023

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision.

[BibT_eX]

[DOI]

CoRR, 2023

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score.

[BibT_eX]

[DOI]

CoRR, 2023

Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks.

[BibT_eX]

[DOI]

CoRR, 2023

Towards Understanding Feature Learning in Out-of-Distribution Generalization.

[BibT_eX]

[DOI]

CoRR, 2023

Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation.

[BibT_eX]

[DOI]

CoRR, 2023

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Combating Bilateral Edge Noise for Robust Link Prediction.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

FedFed: Feature Distillation against Data Heterogeneity in Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SODA: Robust Training of Test-Time Data Adaptors.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning to Augment Distributions for Out-of-distribution Detection.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

InstanT: Semi-supervised Learning with Instance-dependent Thresholds.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Exploring Model Dynamics for Accumulative Poisoning Discovery.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

A Universal Unbiased Method for Classification from Aggregate Observations.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Detecting Out-of-distribution Data through In-distribution Class Prior.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Moderately Distributional Exploration for Domain Generalization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Combating Exacerbated Heterogeneity for Robust Models in Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Out-of-distribution Detection with Implicit Outlier Transformation.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Holistic Label Correction for Noisy Multi-Label Classification.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Hard Sample Matters a Lot in Zero-Shot Quantization.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

NoiLin: Improving adversarial training and correcting stereotype of noisy labels.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2022

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2022

Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Learning from Noisy Pairwise Similarity and Unlabeled Data.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Strength-Adaptive Adversarial Training.

[BibT_eX]

[DOI]

CoRR, 2022

Towards Lightweight Black-Box Attacks against Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2022

Efficient Private SCO for Heavy-Tailed Data via Clipping.

[BibT_eX]

[DOI]

CoRR, 2022

Pareto Invariant Risk Minimization.

[BibT_eX]

[DOI]

CoRR, 2022

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks.

[BibT_eX]

[DOI]

CoRR, 2022

MSR: Making Self-supervised learning Robust to Aggressive Augmentations.

[BibT_eX]

[DOI]

CoRR, 2022

Learning Adaptive Propagation for Knowledge Graph Reasoning.

[BibT_eX]

[DOI]

CoRR, 2022

Robust Weight Perturbation for Adversarial Training.

[BibT_eX]

[DOI]

CoRR, 2022

FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2022

Pluralistic Image Completion with Probabilistic Mixture-of-Experts.

[BibT_eX]

[DOI]

CoRR, 2022

Invariance Principle Meets Out-of-Distribution Generalization on Graphs.

[BibT_eX]

[DOI]

CoRR, 2022

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

[BibT_eX]

[DOI]

CoRR, 2022

Counterfactual Fairness with Partially Known Causal Graph.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Pluralistic Image Completion with Gaussian Mixture Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Watermarking for Out-of-distribution Detection.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Lightweight Black-Box Attack Against Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Exact Shape Correspondence via 2D graph convolution.

[BibT_eX]

[DOI]

Barakeel Fanseu Kamhoua

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Is Out-of-Distribution Detection Learnable?

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Synergy-of-Experts: Collaborate to Improve Adversarial Robustness.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Device-cloud Collaborative Recommendation via Meta Controller.

[BibT_eX]

[DOI]

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks.

[BibT_eX]

[DOI]

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Robust Weight Perturbation for Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Contrastive Learning with Boosted Memorization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Modeling Adversarial Noise for Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Improving Adversarial Robustness via Mutual Information Estimation.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Understanding Robust Overfitting of Adversarial Training and Beyond.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Reliable Adversarial Distillation with Unreliable Teachers.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Adversarial Robustness Through the Lens of Causality.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Exploiting Class Activation Value for Partial-Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Rethinking Class-Prior Estimation for Positive-Unlabeled Learning.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2022, 2022

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Fair Classification with Instance-dependent Label Noise.

[BibT_eX]

[DOI]

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Learning and Mining with Noisy Labels.

[BibT_eX]

[DOI]

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Privacy-Preserving Stochastic Gradual Learning.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2021

Click-through Rate Prediction with Auto-Quantized Contrastive Learning.

[BibT_eX]

[DOI]

CoRR, 2021

MC$^2$-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation.

[BibT_eX]

[DOI]

CoRR, 2021

Modelling Adversarial Noise for Adversarial Defense.

[BibT_eX]

[DOI]

CoRR, 2021

Local Reweighting for Adversarial Training.

[BibT_eX]

[DOI]

CoRR, 2021

PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2021

KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation.

[BibT_eX]

[DOI]

William Kwok-Wai Cheung

CoRR, 2021

Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training.

[BibT_eX]

[DOI]

CoRR, 2021

Instance Correction for Learning with Open-set Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2021

NoiLIn: Do Noisy Labels Always Hurt Adversarial Training?

[BibT_eX]

[DOI]

CoRR, 2021

Estimating Instance-dependent Label-noise Transition Matrix using DNNs.

[BibT_eX]

[DOI]

CoRR, 2021

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data.

[BibT_eX]

[DOI]

CoRR, 2021

Understanding the Interaction of Adversarial Training with Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2021

Instance-dependent Label-noise Learning under a Structural Causal Model.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Probabilistic Margins for Instance Reweighting in Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Universal Semi-Supervised Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Understanding and Improving Early Stopping for Learning with Noisy Labels.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Device-Cloud Collaborative Learning for Recommendation.

[BibT_eX]

[DOI]

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Towards Defending against Adversarial Examples via Attack-Invariant Features.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Provably End-to-end Label-noise Learning without Anchor Points.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Pointwise Binary Classification with Pairwise Confidence Comparisons.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Diverse-Structured Networks for Adversarial Robustness.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Confidence Scores Make Instance-dependent Label-noise Learning Possible.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Geometry-aware Instance-reweighted Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Robust early-learning: Hindering the memorization of noisy labels.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Fraud Detection under Multi-Sourced Extremely Noisy Annotations.

[BibT_eX]

[DOI]

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

HyperGraph Convolution Based Attributed HyperGraph Clustering.

[BibT_eX]

[DOI]

Barakeel Fanseu Kamhoua

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

Learning with Group Noise.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2020

A Survey of Label-noise Representation Learning: Past, Present and Future.

[BibT_eX]

[DOI]

CoRR, 2020

Maximum Mean Discrepancy is Aware of Adversarial Attacks.

[BibT_eX]

[DOI]

CoRR, 2020

Parts-dependent Label Noise: Towards Instance-dependent Label Noise.

[BibT_eX]

[DOI]

CoRR, 2020

Class2Simi: A New Perspective on Learning with Label Noise.

[BibT_eX]

[DOI]

CoRR, 2020

Multi-Class Classification from Noisy-Similarity-Labeled Data.

[BibT_eX]

[DOI]

CoRR, 2020

Towards Mixture Proportion Estimation without Irreducibility.

[BibT_eX]

[DOI]

CoRR, 2020

Confusable Learning for Large-Class Few-Shot Classification.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Part-dependent Label Noise: Towards Instance-dependent Label Noise.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Provably Consistent Partial-Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery.

[BibT_eX]

[DOI]

Yijing Luo

Bo Han

Chen Gong

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Searching to Exploit Memorization Effect in Learning with Noisy Labels.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Variational Imitation Learning with Diverse-quality Demonstrations.

[BibT_eX]

[DOI]

Voot Tangkaratt

Bo Han

Mohammad Emtiyaz Khan

Masashi Sugiyama

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning with Multiple Complementary Labels.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling.

[BibT_eX]

[DOI]

Chao Li

Mohammad Emtiyaz Khan

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Beyond Majority Voting: A Coarse-to-Fine Label Filtration for Heavily Noisy Labels.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2019

Millionaire: a hint-guided approach for crowdsourcing.

[BibT_eX]

[DOI]

Mach. Learn., 2019

Where is the Bottleneck of Adversarial Learning with Unlabeled Data?

[BibT_eX]

[DOI]

CoRR, 2019

Searching to Exploit Memorization Effect in Learning from Corrupted Labels.

[BibT_eX]

[DOI]

CoRR, 2019

VILD: Variational Imitation Learning with Diverse-quality Demonstrations.

[BibT_eX]

[DOI]

Voot Tangkaratt

Bo Han

Mohammad Emtiyaz Khan

Masashi Sugiyama

CoRR, 2019

Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation.

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

CoRR, 2019

Are Anchor Points Really Indispensable in Label-Noise Learning?

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Towards Robust ResNet: A Small Step but a Giant Leap.

[BibT_eX]

[DOI]

Jingfeng Zhang

Bo Han

Laura Wynter

Bryan Kian Hsiang Low

Mohan S. Kankanhalli

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

How does Disagreement Help Generalization against Label Corruption?

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations.

[BibT_eX]

[DOI]

Quanming Yao

James Tin-Yau Kwok

Bo Han

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Progressive Stochastic Learning for Noisy Labels.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2018

Stagewise learning for noisy k-ary preferences.

[BibT_eX]

[DOI]

Yuangang Pan

Bo Han

Ivor W. Tsang

Mach. Learn., 2018

Robust Plackett-Luce model for k-ary crowdsourced preferences.

[BibT_eX]

[DOI]

Bo Han

Yuangang Pan

Ivor W. Tsang

Mach. Learn., 2018

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

[BibT_eX]

[DOI]

CoRR, 2018

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

[BibT_eX]

[DOI]

CoRR, 2018

Co-sampling: Training Robust Networks for Extremely Noisy Supervision.

[BibT_eX]

[DOI]

CoRR, 2018

Co-teaching: Robust training of deep neural networks with extremely noisy labels.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Masking: A New Perspective of Noisy Supervision.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2016

On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent.

[BibT_eX]

[DOI]

Bo Han

Ivor W. Tsang

Ling Chen

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Bo Han

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