We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action
spaces where conventional importance-weighting approaches suffer from excessive …

We study off-policy evaluation (OPE) in the problem of slate contextual bandits where a
policy selects multi-dimensional actions known as slates. This problem is widespread in …

Motivated by practical needs in online experimentation and off-policy learning, we study the
problem of safe optimal design, where we develop a data logging policy that efficiently …

We study a novel variant of the parameterized bandits problem in which the learner can
observe additional auxiliary feedback that is correlated with the observed reward. The …

Recommendation strategies are typically evaluated by using previously logged data,
employing off-policy evaluation methods to estimate their expected performance. However …

Motivated by practical needs of experimentation and policy learning in online platforms, we
study the problem of safe data collection. Specifically, our goal is to develop a logging policy …

In value-based deep reinforcement learning (RL), value function approximation errors lead
to suboptimal policies. Temporal difference (TD) learning is one of the most important …