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20232025
most citedBandits Meet Mechanism Design to Combat Clickbait in Online Recommendation

1 citations · 1 across the 5 of their papers we have counts for

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cs.LG2025

Sequential Cohort Selection under Uncertainty

Hortence Yiepnou, Christos Dimitrakakis

We study the problem of fair cohort selection under uncertainty, motivated by university admissions where applicant outcomes are only partially observed. We consider both a one-sho…

cs.LG2025

Two-Player Zero-Sum Games with Bandit Feedback

Elif Yılmaz, Christos Dimitrakakis

We study a two-player zero-sum game in which the row player aims to maximize their payoff against a competing column player, under an unknown payoff matrix estimated through bandit…

cs.LG2024

Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret

Emilio Jorge, Christos Dimitrakakis, Debabrota Basu

Common assumptions, like linear or RKHS models, and Gaussian or log-concave posteriors over the models, do not explain practical success of RL across a wider range of distributions…

cs.LG2024

Strategic Linear Contextual Bandits

Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis +1

Motivated by the phenomenon of strategic agents gaming a recommender system to maximize the number of times they are recommended to users, we study a strategic variant of the linea…

cs.LG20231 cited

Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation

Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis +1

We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit. This model is motivated by applications in online recommendation where the…