11 citations · 13 across the 6 of their papers we have counts for
6 papers · 1 filter
Near-Linear MIR Algorithms for Stochastically-Ordered Priors
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown +1
With the rise of online applications, recommender systems (RSs) often encounter constraints in balancing exploration and exploitation. Such constraints arise when exploration is ca…
Learning under Invariable Bayesian Safety
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown +1
A recent body of work addresses safety constraints in explore-and-exploit systems. Such constraints arise where, for example, exploration is carried out by individuals whose welfar…
Fiduciary Bandits
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown +1
Recommendation systems often face exploration-exploitation tradeoffs: the system can only learn about the desirability of new options by recommending them to some user. Such system…
Recommendation Systems and Self Motivated Users
Gal Bahar, Rann Smorodinsky, Moshe Tennenholtz
Modern recommendation systems rely on the wisdom of the crowd to learn the optimal course of action. This induces an inherent mis-alignment of incentives between the system's objec…
Multi-Issue Social Learning
Gal Bahar, Itai Arieli, Rann Smorodinsky +1
We consider social learning where agents can only observe part of the population (modeled as neighbors on an undirected graph), face many decision problems, and arrival order of th…
Economic Recommendation Systems
Gal Bahar, Rann Smorodinsky, Moshe Tennenholtz
In the on-line Explore and Exploit literature, central to Machine Learning, a central planner is faced with a set of alternatives, each yielding some unknown reward. The planner's…