23 citations · 49 across the 3 of their papers we have counts for
5 papers
Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)
Shailesh Bavadekar, Andrew Dai, John Davis +27
This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset (published at https://goo.gle/covid19sympt…
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Chang Li, Ilya Markov, Maarten de Rijke +1
Online ranker evaluation is one of the key challenges in information retrieval. While the preferences of rankers can be inferred by interleaving methods, the problem of how to effe…
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
Chang Li, Branislav Kveton, Tor Lattimore +4
In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists. Learning to rank has traditionally be…
Copeland Dueling Bandits
Masrour Zoghi, Zohar Karnin, Shimon Whiteson +1
A version of the dueling bandit problem is addressed in which a Condorcet winner may not exist. Two algorithms are proposed that instead seek to minimize regret with respect to the…
Contextual Dueling Bandits
Miroslav Dudík, Katja Hofmann, Robert E. Schapire +2
We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this p…