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Found 4 papers, 2 papers with code
The Potential of Learned Index Structures for Index Compression
Second, we evaluate the potential gains that can be achieved in terms of memory requirements.
Optimizing Ranking Models in an Online Setting
Our findings show that the theoretical bounds of DBGD do not apply to any common ranking model and, furthermore, that the performance of DBGD is substantially worse than PDGD in both ideal and worst-case circumstances.
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
Conversely, simpler models can be optimized on fewer interactions and thus provide a better user experience, but they will converge towards suboptimal rankings.
Sensitive and Scalable Online Evaluation with Theoretical Guarantees
We show empirically that, compared to previous multileaved comparison methods, PPM is more sensitive to user preferences and scalable with the number of rankers being compared.
