Code

Please read before downloading

  • Please thoroughly read the terms and conditions in the License and agree to them before using the code.

  • If you use this code in your research, please cite the relevant publications given along with the links.

  • I would be happy to receive comments, feedback on the code, as well as the underlying methods.

Research Code

  1. Parabel: Partitioned Label Trees for Extreme Classification with Application to Dynamic Search Advertising,
    Yashoteja Prabhu, Anil Kag, Shrutendra Harsola, Rahul Agrawal, and Manik Varma,
    The Web Conference (WWW), 2018.
    [code]

  2. Extreme Multi-label Learning with Label Features for Warm-Start Tagging, Ranking and Recommendation,
    Yashoteja Prabhu, Anil Kag, Shilpa Gopinath, Shrutendra Harsola, Rahul Agrawal, and Manik Varma,
    International Conference on Web Search and Data Mining (WSDM), 2018.
    [code]

  3. Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking and Other Missing Label Applications,
    Himanshu Jain, Yashoteja Prabhu, and Manik Varma,
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016.
    [code]

  4. FastXML: A Fast, Accurate and Stable Tree-classi´Čüer for eXtreme Multi-label Learning,
    Yashoteja Prabhu, and Manik Varma,
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2014.
    [code]