1 citations · 1 across the 5 of their papers we have counts for
6 papers
Effective Game-Theoretic Motion Planning via Nested Search
Avishav Engle, Andrey Zhitnikov, Oren Salzman +2
To facilitate effective, safe deployment in the real world, individual robots must reason about interactions with other agents, which often occur without explicit communication. Re…
Bandits with Single-Peaked Preferences and Limited Resources
Omer Ben-Porat, Gur Keinan, Rotem Torkan
We study an online stochastic matching problem in which an algorithm sequentially matches users to arms, aiming to maximize cumulative reward over rounds under budget c…
Churn-Aware Recommendation Planning under Aggregated Preference Feedback
Gur Keinan, Omer Ben-Porat
We study a sequential decision-making problem motivated by recent regulatory and technological shifts that limit access to individual user data in recommender systems (RSs), leavin…
Strategic Content Creation with Age of GenAI: To Share or Not to Share?
Gur Keinan, Omer Ben-Porat
We introduce a game-theoretic framework examining strategic interactions between a platform and its content creators in the presence of AI-generated content. Our model's main novel…
Envious Explore and Exploit
Omer Ben-Porat, Yotam Gafni, Or Markovetzki
Explore-and-exploit tradeoffs play a key role in recommendation systems (RSs), aiming at serving users better by learning from previous interactions. Despite their commercial succe…
Modeling Churn in Recommender Systems with Aggregated Preferences
Gur Keinan, Omer Ben-Porat
While recommender systems (RSs) traditionally rely on extensive individual user data, regulatory and technological shifts necessitate reliance on aggregated user information. This…