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Tianxiang Zhao

Orcid: 0000-0003-4504-7809

Affiliations:

Pennsylvania State University, State College, PA, USA

According to our database¹, Tianxiang Zhao authored at least 39 papers between 2020 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

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Links

Online presence:

On csauthors.net:

Bibliography

2025

A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models.

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Vasant G. Honavar

CoRR, July, 2025

Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning.

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CoRR, March, 2025

Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions.

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Vasant G. Honavar

CoRR, January, 2025

Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs.

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Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025

SmGNN: Link Prediction in Sparse Layers of Multi-layer Graphs.

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Proceedings of the 17th ACM Web Science Conference 2025, 2025

Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning.

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Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

DiaLLMs: EHR-Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction.

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Vasant G. Honavar

Proceedings of the Findings of the Association for Computational Linguistics, 2025

2024

Imbalanced Node Classification With Synthetic Over-Sampling.

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IEEE Trans. Knowl. Data Eng., December, 2024

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability.

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Mach. Intell. Res., December, 2024

Towards Inductive and Efficient Explanations for Graph Neural Networks.

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

Enhance Graph Alignment for Large Language Models.

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CoRR, 2024

Enhancing Data-Limited Graph Neural Networks by Actively Distilling Knowledge from Large Language Models.

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CoRR, 2024

Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning.

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CoRR, 2024

Disambiguated Node Classification with Graph Neural Networks.

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Proceedings of the ACM on Web Conference 2024, 2024

Interpretable Imitation Learning with Dynamic Causal Relations.

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Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels.

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Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling.

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Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

HC-GST: Heterophily-aware Distribution Consistency based Graph Self-training.

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Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models.

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Proceedings of the IEEE International Conference on Big Data, 2024

AmGNN: A Framework for Adaptive Processing of Inter-layer Information in Multi-layer Graph.

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Proceedings of the Social Networks Analysis and Mining - 16th International Conference, 2024

2023

Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment.

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ACM Trans. Intell. Syst. Technol., October, 2023

Dynamic DAG Discovery for Interpretable Imitation Learning.

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CoRR, 2023

Towards Faithful and Consistent Explanations for Graph Neural Networks.

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Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network.

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Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations.

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

T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data.

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Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

2022

Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks.

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CoRR, 2022

On Consistency in Graph Neural Network Interpretation.

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CoRR, 2022

Exploring Edge Disentanglement for Node Classification.

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Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features.

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Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

TopoImb: Toward Topology-Level Imbalance in Learning From Graphs.

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Proceedings of the Learning on Graphs Conference, 2022

Explanation Guided Contrastive Learning for Sequential Recommendation.

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

2021

Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases.

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Frontiers Big Data, 2021

Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network.

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CoRR, 2021

You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features.

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CoRR, 2021

GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks.

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Proceedings of the WSDM '21, 2021

2020

Emotion Embedded Pose Generation.

[BibT_eX]

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Amogh Subbakrishna Adishesha

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Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

Semi-Supervised Graph-to-Graph Translation.

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Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

Balancing Quality and Human Involvement: An Effective Approach to Interactive Neural Machine Translation.

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

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