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We consider the problem of active coarse ranking, where the goal is to sort items according to their means into clusters of pre-specified sizes, by adaptively sampling from their reward distributions.
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L. L. Thurstone · 1927
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Computing with noisy information
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Using confidence bounds for exploitation-exploration trade-offs
P. Auer · 2002
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Sorting with unreliable comparisons: A probabilistic analysis
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Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems
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Aggregating inconsistent information: ranking and clustering
N. Ailon, M. Charikar, and A. Newman · 2008
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Sorting from noisy information
M. Braverman and E. Mossel · 2009
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Best arm identification in multi-armed bandits
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The kl-ucb algorithm for bounded stochastic bandits and beyond
A. Garivier and O. Cappé · 2011
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Active ranking using pairwise comparisons
K. G. Jamieson and R. Nowak · 2011
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Algorithms
R. Sedgewick and K. Wayne · 2011
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An active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
S. Bubeck, N. Cesa-Bianchi, et al · 2012
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Elements of information theory
T. M. Cover and J. A. Thomas · 2012
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Pac subset selection in stochastic multi-armed bandits
S. Kalyanakrishnan, A. Tewari, P. Auer, and P. Stone · 2012
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Multiple identifications in multi-armed bandits
S. Bubeck, T. Wang, and N. Viswanathan · 2013
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Information complexity in bandit subset selection
E. Kaufmann and S. Kalyanakrishnan · 2013
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On the complexity of best arm identification in multi-armed bandit models
E. Kaufmann, O. Cappé, and A. Garivier · 2015
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Ranking from stochastic pairwise preferences: Recovering condorcet winners and tournament solution sets at the top
A. Rajkumar, S. Ghoshal, L.-H. Lim, and S. Agarwal · 2015
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On ranking and choice models
S. Agarwal · 2016
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Deep learning the city: Quantifying urban perception at a global scale
A. Dubey, N. Naik, D. Parikh, R. Raskar, and C. A. Hidalgo · 2016
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Active ranking from pairwise comparisons and the futility of parametric assumptions
R. Heckel, N. B. Shah, K. Ramchandran, and M. J. Wainwright · 2016
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Efficient ranking from pairwise comparisons
F. Wauthier, M. Jordan, and N. Jojic · 2013
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Towards a social functional account of laughter: Acoustic features convey reward, affiliation, and dominance
A. Wood, J. Martin, and P. Niedenthal · 2013
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A survey of preference-based online learning with bandit algorithms
R. Busa-Fekete and E. Hüllermeier · 2014
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Streetscore-predicting the perceived safety of one million streetscapes
N. Naik, J. Philipoom, R. Raskar, and C. Hidalgo · 2014
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A statistical convergence perspective of algorithms for rank aggregation from pairwise data
A. Rajkumar and S. Agarwal · 2014
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Next: A system for real-world development, evaluation, and application of active learning
K. G. Jamieson, L. Jain, C. Fernandez, N. J. Glattard, and R. Nowak
Cited in the paper.
N. Shah, S. Balakrishnan, A. Guntuboyina, and M. Wainwright · 2016
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Learning with limited rounds of adaptivity: Coin tossing, multi-armed bandits, and ranking from pairwise comparisons
A. Agarwal, S. Agarwal, S. Assadi, and S. Khanna · 2017
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Nearly instance optimal sample complexity bounds for top-k arm selection
L. Chen, J. Li, and M. Qiao · 2017
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Maximum selection and ranking under noisy comparisons
M. Falahatgar, A. Orlitsky, V. Pichapati, and A. T. Suresh · 2017
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Practical algorithms for best-k identification in multi-armed bandits
H. Jiang, J. Li, and M. Qiao · 2017
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Just sort it! a simple and effective approach to active preference learning
L. Maystre and M. Grossglauser · 2017
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