Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling

Stochastic Rank-One Bandits (Katarya et al, (2017a,b)) are a simple framework for regret minimization problems over rank-one matrices of arms. The initially proposed algorithms are proved to have logarithmic regret, but do not match the existing lower bound for this problem. We close this gap by fir...

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Hauptverfasser:	Trinh, Cindy, Kaufmann, Emilie, Vernade, Claire, Combes, Richard
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
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