61 citations · 252 across the 19 of their papers we have counts for
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cs.SI2020★ 47 cited
From Optimizing Engagement to Measuring Value
Smitha Milli, Luca Belli, Moritz Hardt
Most recommendation engines today are based on predicting user engagement, e.g. predicting whether a user will click on an item or not. However, there is potentially a large gap be…
cs.LG2020★ 56 cited
Reward-rational (implicit) choice: A unifying formalism for reward learning
Hong Jun Jeon, Smitha Milli, Anca D. Dragan
It is often difficult to hand-specify what the correct reward function is for a task, so researchers have instead aimed to learn reward functions from human behavior or feedback. T…