Direct Learning to Rank and Rerank
Publication
, Conference
Rudin, C; Wang, Y
Published in: Proceedings of Machine Learning Research
January 1, 2018
Learning-to-rank techniques have proven to be extremely useful for prioritization problems, where we rank items in order of their estimated probabilities, and dedicate our limited resources to the top-ranked items. This work exposes a serious problem with the state of learning-to-rank algorithms, which is that they are based on convex proxies that lead to poor approximations. We then discuss the possibility of “exact” reranking algorithms based on mathematical programming. We prove that a relaxed version of the “exact” problem has the same optimal solution, and provide an empirical analysis.
Duke Scholars
Published In
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2018
Volume
84
Citation
APA
Chicago
ICMJE
MLA
NLM
Rudin, C., & Wang, Y. (2018). Direct Learning to Rank and Rerank. In Proceedings of Machine Learning Research (Vol. 84).
Rudin, C., and Y. Wang. “Direct Learning to Rank and Rerank.” In Proceedings of Machine Learning Research, Vol. 84, 2018.
Rudin C, Wang Y. Direct Learning to Rank and Rerank. In: Proceedings of Machine Learning Research. 2018.
Rudin, C., and Y. Wang. “Direct Learning to Rank and Rerank.” Proceedings of Machine Learning Research, vol. 84, 2018.
Rudin C, Wang Y. Direct Learning to Rank and Rerank. Proceedings of Machine Learning Research. 2018.
Published In
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2018
Volume
84