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. Extreme Regression for Dynamic Search Advertising,
   Yashoteja Prabhu, Aditya Kusupati, Nilesh Gupta and Manik Varma,
   ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020.
   [code]

2. 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]

3. 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]

4. 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]

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