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Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling
Real-world applications of contextual bandits often exhibit non-stationarity due to seasonality, serendipity, and evolving social trends.
A Definition of Non-Stationary Bandits
Despite the subject of non-stationary bandit learning having attracted much recent attention, we have yet to identify a formal definition of non-stationarity that can consistently distinguish non-stationary bandits from stationary ones.
Experimenting under Stochastic Congestion
We study randomized experiments in a service system when stochastic congestion can arise from temporarily limited supply and/or demand.
Non-Stationary Bandit Learning via Predictive Sampling
We attribute such failures to the fact that, when exploring, the algorithm does not differentiate actions based on how quickly the information acquired loses its usefulness due to non-stationarity.
Weak Signal Asymptotics for Sequentially Randomized Experiments
We use the lens of weak signal asymptotics to study a class of sequentially randomized experiments, including those that arise in solving multi-armed bandit problems.
