Cascading bandits can be made robust to adversarial click corruption using multi-instance position-based elimination, with regret logarithmic in time and linear in the corruption budget.
Stochastic Linear Optimization with Adversarial Corruption
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abstract
We extend the model of stochastic bandits with adversarial corruption (Lykouriset al., 2018) to the stochastic linear optimization problem (Dani et al., 2008). Our algorithm is agnostic to the amount of corruption chosen by the adaptive adversary. The regret of the algorithm only increases linearly in the amount of corruption. Our algorithm involves using L\"owner-John's ellipsoid for exploration and dividing time horizon into epochs with exponentially increasing size to limit the influence of corruption.
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Cascading Bandits Robust to Adversarial Corruptions
Cascading bandits can be made robust to adversarial click corruption using multi-instance position-based elimination, with regret logarithmic in time and linear in the corruption budget.