no code implementations • 12 Jun 2023 • Qinyi Chen, Jason Cheuk Nam Liang, Negin Golrezaei, Djallel Bouneffouf
Motivated by this, we formulate a novel fair recommendation framework, called Problem (FAIR), that not only maximizes the platform's revenue, but also accommodates varying fairness considerations from the perspectives of items and users.
no code implementations • 3 Feb 2023 • Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
In light of this finding, under a bandit feedback setting that mimics real-world scenarios where advertisers have limited information on ad auctions in each channels and how channels procure ads, we present an efficient learning algorithm that produces per-channel budgets whose resulting conversion approximates that of the global optimal problem.
1 code implementation • ICML 2020 • Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis
We consider the use of decision trees for decision-making problems under the predict-then-optimize framework.
no code implementations • 8 Nov 2019 • Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang
We show that this design allows the seller to control the number of periods in which buyers significantly corrupt their bids.