The Off-Policy Evaluation (OPE) problem consists of evaluating the performance of
counterfactual policies with data collected by another one. This problem is of utmost …

In this paper, we propose a novel $ K $-nearest neighbor resampling procedure for
estimating the performance of a policy from historical data containing realized episodes of a …

Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in
which a centrally coordinated fleet of self-driving vehicles dynamically serves travel …

Off-policy evaluation (OPE) is to evaluate a target policy with data generated by other
policies. Most previous OPE methods focus on precisely estimating the true performance of …

Many sequential decision making problems are high-stakes and require off-policy
evaluation (OPE) of a new policy using historical data collected using some other policy …

We revisit the classical causal inference problem of estimating the average treatment effect
in the presence of fully observed confounding variables using two-stage semiparametric …

We propose a speech-driven laughter backchannel generation model to reward
engagement during human-agent interaction. We formulate the problem as a Markov …

In this work, we consider the off-policy policy evaluation problem for contextual bandits and
finite horizon reinforcement learning in the nonstationary setting. Reusing old data is critical …

Reinforcement Learning (RL) has recently found many applications in the healthcare
domain thanks to its natural fit to clinical decision-making and ability to learn optimal …

Short-and long-term outcomes of an algorithm often differ, with damaging downstream
effects. A known example is a click-bait algorithm, which may increase short-term clicks but …