Xiaofeng Cao

Orcid: 0000-0001-5816-1059

Affiliations:
  • Jilin University, School of Artificial Intelligence, Changchun, China
  • University of Technology Sydney, Australian Artificial Intelligence Institute, Advanced Analytics Institute, Australia (PhD 2021)


According to our database1, Xiaofeng Cao authored at least 36 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Diversifying Policies With Non-Markov Dispersion to Expand the Solution Space.
IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

Transductive Reward Inference on Graph.
IEEE Trans. Knowl. Data Eng., November, 2024

Refining Euclidean Obfuscatory Nodes Helps: A Joint-Space Graph Learning Method for Graph Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., September, 2024

Distribution Matching for Machine Teaching.
IEEE Trans. Neural Networks Learn. Syst., September, 2024

Improving Augmentation Consistency for Graph Contrastive Learning.
Pattern Recognit., April, 2024

Enhancing Locally Adaptive Smoothing of Graph Neural Networks Via Laplacian Node Disagreement.
IEEE Trans. Knowl. Data Eng., March, 2024

Hyperbolic Uncertainty Aware Semantic Segmentation.
IEEE Trans. Intell. Transp. Syst., February, 2024

Breaking the curse of dimensional collapse in graph contrastive learning: A whitening perspective.
Inf. Sci., February, 2024

Sharpness-Aware Minimization Activates the Interactive Teaching's Understanding and Optimization.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

MetaRepair: Learning to Repair Deep Neural Networks from Repairing Experiences.
Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023
Data-Efficient Learning via Minimizing Hyperspherical Energy.
IEEE Trans. Pattern Anal. Mach. Intell., November, 2023

Towards fidelity of graph data augmentation via equivariance.
Knowl. Based Syst., November, 2023

Taming over-smoothing representation on heterophilic graphs.
Inf. Sci., November, 2023

Improving generalization of double low-rank representation using Schatten-<i>p</i> norm.
Pattern Recognit., June, 2023

AdaNS: Adaptive negative sampling for unsupervised graph representation learning.
Pattern Recognit., April, 2023

Aggregation Weighting of Federated Learning via Generalization Bound Estimation.
CoRR, 2023

Nonparametric Teaching for Multiple Learners.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Nonparametric Iterative Machine Teaching.
Proceedings of the International Conference on Machine Learning, 2023

Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships.
Proceedings of the International Conference on Machine Learning, 2023

2022
Shattering Distribution for Active Learning.
IEEE Trans. Neural Networks Learn. Syst., 2022

Cold-Start Active Sampling Via γ-Tube.
IEEE Trans. Cybern., 2022

Distribution Disagreement via Lorentzian Focal Representation.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Robust active representation via ℓ2, p-norm constraints.
Knowl. Based Syst., 2022

One-shot Machine Teaching: Cost Very Few Examples to Converge Faster.
CoRR, 2022

A Survey of Learning on Small Data.
CoRR, 2022

When an Active Learner Meets a Black-box Teacher.
CoRR, 2022

2021
Distribution-based Active Learning
PhD thesis, 2021

High-dimensional cluster boundary detection using directed Markov tree.
Pattern Anal. Appl., 2021

Bayesian Active Learning by Disagreements: A Geometric Perspective.
CoRR, 2021

2020
A structured perspective of volumes on active learning.
Neurocomputing, 2020

A divide-and-conquer approach to geometric sampling for active learning.
Expert Syst. Appl., 2020

2019
Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data.
IEEE Trans. Neural Networks Learn. Syst., 2019

BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data.
Pattern Anal. Appl., 2019

Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019


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