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Computer Science

D-Index
74
Citations
27321
World Ranking
1468
National Ranking
763

Overview

Xifeng Yan is a researcher affiliated with the University of California, Santa Barbara in the United States. Their work primarily covers the field of Computer Science, with a significant focus on Artificial Intelligence, as well as notable contributions to Computer Vision and Pattern Recognition, Materials Chemistry, Information Systems, and Modeling and Simulation.

The main research topics explored by Xifeng Yan include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Advanced Graph Neural Networks
  • COVID-19 Epidemiological Studies
  • Titanium Alloys Microstructure and Properties

Among the recent papers associated with this researcher are:

  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States, 2022, Proceedings of the National Academy of Sciences
  • The United States COVID-19 Forecast Hub dataset, 2022, Scientific Data
  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US, 2021, bioRxiv (Cold Spring Harbor Laboratory)
  • CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers, 2020, arXiv (Cornell University)
  • Inductive Relation Prediction by BERT, 2022, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent co-authors collaborating with Xifeng Yan include:

  • Jiajun Bu
  • Weizhi Wang
  • Xiaoyong Jin
  • Zekun Li
  • Yu-Xiang Wang

The majority of Xifeng Yan's publications appear in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of Alloys and Compounds
  • Zenodo (CERN European Organization for Nuclear Research)
  • SSRN Electronic Journal

Best Publications

  • gSpan: graph-based substructure pattern mining

    Xifeng Yan;Jiawei Han

  • PathSim: meta path-based top-K similarity search in heterogeneous information networks

    Yizhou Sun;Jiawei Han;Xifeng Yan;Philip S. Yu

  • Frequent pattern mining: current status and future directions

    Jiawei Han;Hong Cheng;Dong Xin;Xifeng Yan

  • Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

    Shiyang Li;Xiaoyong Jin;Yao Xuan;Xiyou Zhou

  • CloSpan: Mining Closed Sequential Patterns in Large Datasets

    Unknown

  • Graph indexing: a frequent structure-based approach

    Xifeng Yan;Philip S. Yu;Jiawei Han

  • CloseGraph: mining closed frequent graph patterns

    Xifeng Yan;Jiawei Han

  • Mining Frequent Patterns in Data Streams at Multiple Time Granularities

    Chris Giannella;Jiawei Han;Xifeng Yan;Philip S. Yu

  • SOBER: statistical model-based bug localization

    Chao Liu;Xifeng Yan;Long Fei;Jiawei Han

  • Discriminative Frequent Pattern Analysis for Effective Classification

    Hong Cheng;Xifeng Yan;Jiawei Han;Chih-Wei Hsu

  • Mining coherent dense subgraphs across massive biological networks for functional discovery

    Haiyan Hu;Xifeng Yan;Yu Huang;Jiawei Han

  • Substructure similarity search in graph databases

    Xifeng Yan;Philip S. Yu;Jiawei Han

  • PathSelClus: Integrating Meta-Path Selection with User-Guided Object Clustering in Heterogeneous Information Networks

    Yizhou Sun;Brandon Norick;Jiawei Han;Xifeng Yan

  • Statistical Debugging: A Hypothesis Testing-Based Approach

    Chao Liu;Long Fei;Xifeng Yan;Jiawei Han

  • Mining significant graph patterns by leap search

    Xifeng Yan;Hong Cheng;Jiawei Han;Philip S. Yu

  • Workload characterization and prediction in the cloud: A multiple time series approach

    Arijit Khan;Xifeng Yan;Shu Tao;Nikos Anerousis

  • TSP: Mining top-k closed sequential patterns

    Petre Tzvetkov;Xifeng Yan;Jiawei Han

  • IncSpan: incremental mining of sequential patterns in large database

    Hong Cheng;Xifeng Yan;Jiawei Han

  • Mining compressed frequent-pattern sets

    Dong Xin;Jiawei Han;Xifeng Yan;Hong Cheng

  • Synthesizing Near-Optimal Malware Specifications from Suspicious Behaviors

    Matt Fredrikson;Somesh Jha;Mihai Christodorescu;Reiner Sailer

  • Direct Discriminative Pattern Mining for Effective Classification

    Hong Cheng;Xifeng Yan;Jiawei Han;P.S. Yu

Frequent Co-Authors

Jiawei Han
Jiawei Han University of Illinois at Urbana-Champaign
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Yu Su
Yu Su The Ohio State University
Feida Zhu
Feida Zhu Singapore Management University
Mudhakar Srivatsa
Mudhakar Srivatsa IBM (United States)
Ambuj K. Singh
Ambuj K. Singh University of California, Santa Barbara
William Yang Wang
William Yang Wang University of California, Santa Barbara
Yizhou Sun
Yizhou Sun University of California, Los Angeles
Amr El Abbadi
Amr El Abbadi University of California, Santa Barbara
Karsten M. Borgwardt
Karsten M. Borgwardt Max Planck Institute of Biochemistry

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