People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics.
Existing works focus on multi-armed bandit with static preference, but this is insufficient: the two-sided preference changes as along as one-side's contextual information updates, resulting in non-static matching.
The recent emergence of reinforcement learning has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms.
Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a study of statistical data deletion problems where users' data are accessible only for a limited period of time.
Estimating parameters of mixture model has wide applications ranging from classification problems to estimating of complex distributions.