Y
Yashoteja Prabhu
Researcher at Indian Institute of Technology Delhi
Publications - 9
Citations - 1217
Yashoteja Prabhu is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Ranking (information retrieval) & Ranking. The author has an hindex of 6, co-authored 8 publications receiving 966 citations. Previous affiliations of Yashoteja Prabhu include Microsoft.
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Proceedings ArticleDOI
FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning
Yashoteja Prabhu,Manik Varma +1 more
TL;DR: The objective, in this paper, is to develop an extreme multi-label classifier that is faster to train and more accurate at prediction than the state-of-the-art Multi-label Random Forest algorithm and the Label Partitioning for Sub-linear Ranking algorithm.
Proceedings ArticleDOI
Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications
TL;DR: In this article, the authors propose propensity scored loss functions for extreme multi-label learning, which prioritize predicting the few relevant labels over the large number of irrelevant ones and provide unbiased estimates of the true loss function even when ground truth labels go missing under arbitrary probabilistic label noise models.
Proceedings ArticleDOI
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages
TL;DR: It is demonstrated that it is possible to efficiently predict the relevant subset of queries from a large set of monetizable ones by posing the problem as a multi-label learning task with each query being represented by a separate label.
Proceedings ArticleDOI
Parabel: Partitioned Label Trees for Extreme Classification with Application to Dynamic Search Advertising
TL;DR: Parabel as mentioned in this paper learns a balanced label hierarchy such that the 1-vs-all classifiers in the leaf nodes of the label hierarchy can be trained on a small subset of the training set thereby reducing the training time to a few hours on a single core of a standard desktop.
Proceedings ArticleDOI
Extreme Multi-label Learning with Label Features for Warm-start Tagging, Ranking & Recommendation
Yashoteja Prabhu,Anil Kag,Shilpa Gopinath,Kunal Dahiya,Shrutendra Harsola,Rahul Agrawal,Manik Varma +6 more
TL;DR: The extreme classification problem when predictions need to be made on training points with partially revealed labels is formulates, which allows the reformulation of warm-start tagging, ranking and recommendation problems as extreme multi-label learning with each item to be ranked/recommended being mapped onto a separate label.