← Authors

Bo Han

178 papers · 2010–2026 · 16 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (34) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (15) 🌍 Conference Polyglot (16) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (14) 🏠 Conference Loyalist (46) πŸ”¬ Deep Specialist (33) πŸ‘‘ Triple Crown πŸ† Keyword Champion (4) πŸ† Grand Slam 🀝 Dynamic Duo (90) πŸ“ˆ Trend Setter ❓ The Questioner (10) πŸš€ Conference Pioneer ⚑ Prolific Year (32) πŸ”₯ Unstoppable (8) πŸ—ƒοΈ Keyword Collector (113) πŸ’Ž Century Club (176)

Conferences

ICML (47) NIPS (46) ICLR (38) AAAI (11) IJCAI (8) CVPR (5) ICCV (5) COLING (4) EMNLP (3) JMLR (3) ACL (2) NSDI (2) CLEAR (1) CONLL (1) EACL (1) ECCV (1)

Top co-authors

Tongliang Liu (90) Gang Niu (42) Masashi Sugiyama (33) Jiangchao Yao (24) Feng Liu (21) Yonggang Zhang (20) Mingming Gong (18) Xiaobo Xia (16) qizhou wang (14) Chen Gong (13)

Keywords

label noise (19) noisy label (13) out-of-distribution detection (12) adversarial robustness (12) noisy label learning (10) representation learning (10) adversarial training (8) federated learning (7) causal inference (7) transition matrix (7) distribution shift (7) domain generalization (7) deep neural network (7) semi-supervised learning (6) domain adaptation (6) self-supervised learning (5) contrastive learning (5) weakly supervised learning (5) adversarial example (5) memorization effect (5)

Papers

Transferability of Adversarial Attacks in Video-based MLLMs: A Cross-modal Image-to-Video Approach AAAI 2026
DiCaP: Distribution-Calibrated Pseudo-labeling for Semi-Supervised Multi-Label Learning AAAI 2026
Towards Regularized Mixture of Predictions for Class-Imbalanced Semi-Supervised Facial Expression Recognition IJCAI 2025
From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information? ICML 2025
From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium ICML 2025
Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning ICML 2025
Adaptive Localization of Knowledge Negation for Continual LLM Unlearning ICML 2025
COSDA: Counterfactual-based Susceptibility Risk Framework for Open-Set Domain Adaptation ICML 2025
GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs ICML 2025
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need ICML 2025
A Lens into Interpretable Transformer Mistakes via Semantic Dependency ICML 2025
Learning without Isolation: Pathway Protection for Continual Learning ICML 2025
Instance-dependent Early Stopping ICLR 2025
Understanding and Enhancing the Transferability of Jailbreaking Attacks ICLR 2025
Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond ICLR 2025
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods ICLR 2025
Towards Out-of-Modal Generalization without Instance-level Modal Correspondence ICLR 2025
Fast and Accurate Blind Flexible Docking ICLR 2025
Noisy Test-Time Adaptation in Vision-Language Models ICLR 2025
A Robust Method to Discover Causal or Anticausal Relation ICLR 2025
Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection ICLR 2025
Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Generation ICLR 2025
Golden Noise for Diffusion Models: A Learning Framework ICCV 2025
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning EMNLP 2025
Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models COLING 2025
Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection AAAI 2025
Eliciting Causal Abilities in Large Language Models for Reasoning Tasks AAAI 2025
One-shot Federated Learning Methods: A Practical Guide IJCAI 2025
Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning CVPR 2024
Few-Shot Adversarial Prompt Learning on Vision-Language Models NIPS 2024
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning NIPS 2024
What If the Input is Expanded in OOD Detection? NIPS 2024
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion NIPS 2024
Pseudo-Private Data Guided Model Inversion Attacks NIPS 2024
Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection NIPS 2024
Revive Re-weighting in Imbalanced Learning by Density Ratio Estimation NIPS 2024
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? NIPS 2024
Discovery of the Hidden World with Large Language Models NIPS 2024
A Sober Look at the Robustness of CLIPs to Spurious Features NIPS 2024
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales? NIPS 2024
AMD: Autoregressive Motion Diffusion AAAI 2024
Federated Learning with Extremely Noisy Clients via Negative Distillation AAAI 2024
Enhancing Evolving Domain Generalization through Dynamic Latent Representations AAAI 2024
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations ICLR 2024
Negative Label Guided OOD Detection with Pretrained Vision-Language Models ICLR 2024
Robust Training of Federated Models with Extremely Label Deficiency ICLR 2024
FedImpro: Measuring and Improving Client Update in Federated Learning ICLR 2024
Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel ICLR 2024
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation ICLR 2024
Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs ICLR 2024
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting ICLR 2024
Out-of-Distribution Detection with Negative Prompts ICLR 2024
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting ICLR 2024
Accurate Forgetting for Heterogeneous Federated Continual Learning ICLR 2024
Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy ICLR 2024
Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation ICLR 2024
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection ICML 2024
How Interpretable Are Interpretable Graph Neural Networks? ICML 2024
Towards Realistic Model Selection for Semi-supervised Learning ICML 2024
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency ICML 2024
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence ICML 2024
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning ICML 2024
Mitigating Label Noise on Graphs via Topological Sample Selection ICML 2024
Balancing Similarity and Complementarity for Federated Learning ICML 2024
MCM: Multi-condition Motion Synthesis Framework IJCAI 2024
ParsNets: A Parsimonious Composition of Orthogonal and Low-Rank Linear Networks for Zero-Shot Learning IJCAI 2024
Trustworthy Machine Learning under Imperfect Data IJCAI 2024
On the Learnability of Out-of-distribution Detection JMLR 2024
Habitus: Boosting Mobile Immersive Content Delivery through Full-body Pose Tracking and Multipath Networking NSDI 2024
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? ICML 2023
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning NIPS 2023
InstanT: Semi-supervised Learning with Instance-dependent Thresholds NIPS 2023
Combating Bilateral Edge Noise for Robust Link Prediction NIPS 2023
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis ICCV 2023
Label-Noise Learning with Intrinsically Long-Tailed Data ICCV 2023
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples ICCV 2023
Holistic Label Correction for Noisy Multi-Label Classification ICCV 2023
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization ICLR 2023
Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning ICLR 2023
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning ICLR 2023
A Universal Unbiased Method for Classification from Aggregate Observations ICML 2023
Detecting Out-of-distribution Data through In-distribution Class Prior ICML 2023
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation ICML 2023
Learning to Augment Distributions for Out-of-distribution Detection NIPS 2023
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources NIPS 2023
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? NIPS 2023
Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping NIPS 2023
Understanding and Improving Feature Learning for Out-of-Distribution Generalization NIPS 2023
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning NIPS 2023
Moderately Distributional Exploration for Domain Generalization ICML 2023
Out-of-distribution Detection with Implicit Outlier Transformation ICLR 2023
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style ICLR 2023
A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond ICLR 2023
Combating Representation Learning Disparity with Geometric Harmonization NIPS 2023
Hard Sample Matters a Lot in Zero-Shot Quantization CVPR 2023
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation NIPS 2023
Adjustment and Alignment for Unbiased Open Set Domain Adaptation CVPR 2023
Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization CVPR 2023
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data NIPS 2023
SODA: Robust Training of Test-Time Data Adaptors NIPS 2023
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension AAAI 2023
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability ICML 2023
Exploring Model Dynamics for Accumulative Poisoning Discovery ICML 2023
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation ICML 2023
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score ICML 2023
Watermarking for Out-of-distribution Detection NIPS 2022
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels ICLR 2022
Adversarial Robustness Through the Lens of Causality ICLR 2022
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability ICLR 2022
Reliable Adversarial Distillation with Unreliable Teachers ICLR 2022
Exploiting Class Activation Value for Partial-Label Learning ICLR 2022
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning ICLR 2022
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data ICLR 2022
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization JMLR 2022
Learning from Noisy Pairwise Similarity and Unlabeled Data JMLR 2022
EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs ECCV 2022
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization NIPS 2022
Counterfactual Fairness with Partially Known Causal Graph NIPS 2022
Is Out-of-Distribution Detection Learnable? NIPS 2022
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness NIPS 2022
Pluralistic Image Completion with Gaussian Mixture Models NIPS 2022
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks NIPS 2022
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs NIPS 2022
RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning NIPS 2022
Towards Lightweight Black-Box Attack Against Deep Neural Networks NIPS 2022
Exact Shape Correspondence via 2D graph convolution NIPS 2022
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack ICML 2022
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning ICML 2022
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network ICML 2022
Understanding Robust Overfitting of Adversarial Training and Beyond ICML 2022
Improving Adversarial Robustness via Mutual Information Estimation ICML 2022
Modeling Adversarial Noise for Adversarial Training ICML 2022
Contrastive Learning with Boosted Memorization ICML 2022
Robust Weight Perturbation for Adversarial Training IJCAI 2022
YuZu: Neural-Enhanced Volumetric Video Streaming NSDI 2022
Fair Classification with Instance-dependent Label Noise CLEAR 2022
Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation CVPR 2022
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks ICML 2021
Learning Diverse-Structured Networks for Adversarial Robustness ICML 2021
Confidence Scores Make Instance-dependent Label-noise Learning Possible ICML 2021
Instance-dependent Label-noise Learning under a Structural Causal Model NIPS 2021
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation NIPS 2021
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model AAAI 2021
Learning with Group Noise AAAI 2021
Probabilistic Margins for Instance Reweighting in Adversarial Training NIPS 2021
Understanding and Improving Early Stopping for Learning with Noisy Labels NIPS 2021
Universal Semi-Supervised Learning NIPS 2021
Pointwise Binary Classification with Pairwise Confidence Comparisons ICML 2021
Provably End-to-end Label-noise Learning without Anchor Points ICML 2021
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels ICML 2021
Towards Defending against Adversarial Examples via Attack-Invariant Features ICML 2021
Geometry-aware Instance-reweighted Adversarial Training ICLR 2021
Robust early-learning: Hindering the memorization of noisy labels ICLR 2021
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger ICML 2020
Provably Consistent Partial-Label Learning NIPS 2020
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling AAAI 2020
A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery IJCAI 2020
Learning with Multiple Complementary Labels ICML 2020
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust ICML 2020
Variational Imitation Learning with Diverse-quality Demonstrations ICML 2020
Part-dependent Label Noise: Towards Instance-dependent Label Noise NIPS 2020
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning NIPS 2020
Searching to Exploit Memorization Effect in Learning with Noisy Labels ICML 2020
Are Anchor Points Really Indispensable in Label-Noise Learning? NIPS 2019
How does Disagreement Help Generalization against Label Corruption? ICML 2019
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations ICML 2019
Towards Robust ResNet: A Small Step but a Giant Leap IJCAI 2019
Co-teaching: Robust training of deep neural networks with extremely noisy labels NIPS 2018
Masking: A New Perspective of Noisy Supervision NIPS 2018
A Stacking-based Approach to Twitter User Geolocation Prediction ACL 2013
Automatically Constructing a Normalisation Dictionary for Microblogs EMNLP 2012
Automatically Constructing a Normalisation Dictionary for Microblogs CONLL 2012
A Support Platform for Event Detection using Social Intelligence EACL 2012
Geolocation Prediction in Social Media Data by Finding Location Indicative Words COLING 2012
Lexical Normalisation of Short Text Messages: Makn Sens a #twitter ACL 2011
SRL-Based Verb Selection for ESL EMNLP 2010
Semantic Role Labeling for News Tweets COLING 2010
Collective Semantic Role Labeling on Open News Corpus by Leveraging Redundancy COLING 2010