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Bo HAN, Dr
Assistant Professor
,
Department of Computer Science
https://orcid.org/0000-0002-6338-0958
Email
bhanml
hkbu.edu
hk
Accepting PhD Students
2020
2025
Research activity per year
Overview
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Network
Projects / Grants
(10)
Research Output
(156)
Prizes / Awards
(8)
Activities
(7)
Similar Scholars
(6)
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Dive into the research topics where Bo HAN is active. Topic labels come from the works of this scholar. Together they form a unique fingerprint.
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Keyphrases
Label Noise
100%
Out-of-distribution Detection
80%
Noisy Labels
74%
Adversarial Training
68%
Noisy Label Learning
67%
Federated Learning
61%
Learning with Noisy Labels
60%
Memorization
54%
Deep Neural Network
48%
Transition Matrix
47%
Out-of-distribution Data
41%
Adversarial Data
38%
Learning Methods
37%
Unlabeled Data
37%
Adversarial Robustness
37%
Semi-supervised Learning
31%
Sample Selection
31%
Deep Learning
30%
Out-of-distribution Generalization
27%
Graph Neural Network
24%
Selection Strategy
24%
Labeled Data
23%
Clean Label
22%
Distillation
21%
Adversarial Examples
20%
Overfitting
20%
Benchmark Dataset
19%
Graph Convolutional Network
19%
Data Heterogeneity
19%
Popular
19%
Unbiased Risk Estimation
18%
In-distribution
18%
Training Data
18%
Semi-supervised Method
18%
Real-world Application
17%
Early Stopping
17%
Deep Network
17%
Maximum Mean Discrepancy
17%
Distribution Shift
17%
State-of-the-art Techniques
16%
Learning Paradigm
16%
Open World
16%
Machine Learning
16%
Adversarial Attack
15%
Invariant Features
15%
Novel Class
15%
Robust Overfitting
15%
Outlier Exposure
15%
Robust Learning
15%
Data Distribution
15%
Computer Science
Adversarial Machine Learning
87%
Data Distribution
86%
Deep Neural Network
85%
Federated Learning
70%
Transition Matrix
61%
Machine Learning
53%
Learning System
53%
Experimental Result
51%
Semisupervised Learning
48%
Training Data
43%
Deep Learning Method
40%
Unlabeled Data
37%
Risk Estimator
30%
Graph Neural Network
30%
Annotation
29%
World Application
27%
Learning Algorithm
25%
Adversarial Example
22%
Class Distribution
22%
Selection Process
21%
Learning Framework
20%
Classification Performance
20%
Data Heterogeneity
19%
Risk Minimization
19%
Subgraphs
19%
Supervised Classification
19%
Bilevel Optimisation
19%
Graph Convolution
19%
Clustering Graph
18%
Representation Learning
18%
Sample Distribution
17%
Domain Adaptation
17%
Art Performance
16%
Generalization Performance
16%
Self-Supervised Learning
15%
Capacity Model
15%
Training Model
15%
Language Modeling
15%
Regularization
14%
Problem Setting
14%
Estimation Error
14%
Decision Boundary
14%
Supervised Learning
14%
Approximation (Algorithm)
13%
Independent Instance
13%
Gradient Descent
13%
Distributionally Robust Optimization
13%
Network Parameter
13%
Contrastive Learning
13%
Big Data
13%