1 code implementation • 12 Jan 2024 • Dmitry Ivanov, Omer Ben-Porat
In an r-MDP, we cater to a diverse user population, each with unique preferences, through interaction with a small set of representative policies.
1 code implementation • 30 Dec 2023 • Omer Ben-Porat, Yishay Mansour, Michal Moshkovitz, Boaz Taitler
Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest.
no code implementations • 2 Feb 2023 • Omer Ben-Porat, Rotem Torkan
In this work, we propose a contextual multi-armed bandit setting to model the dependency of content providers on exposure.
no code implementations • 25 Mar 2022 • Omer Ben-Porat, Lee Cohen, Liu Leqi, Zachary C. Lipton, Yishay Mansour
We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal.
no code implementations • NeurIPS 2020 • Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz
Recommendation Systems like YouTube are vibrant ecosystems with two types of users: Content consumers (those who watch videos) and content providers (those who create videos).
no code implementations • ICML 2020 • Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier
We develop several scalable techniques to solve the matching problem, and also draw connections to various notions of user regret and fairness, arguing that these outcomes are fairer in a utilitarian sense.
no code implementations • 8 Jun 2020 • Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz
A recent body of work addresses safety constraints in explore-and-exploit systems.
1 code implementation • 6 Apr 2020 • Omer Ben-Porat, Sharon Hirsch, Lital Kuchy, Guy Elad, Roi Reichart, Moshe Tennenholtz
In ablation analysis, we demonstrate the importance of our modeling choices---the representation of the text with the commonsensical personality attributes and our classifier---to the predictive power of our model.
no code implementations • 20 Jun 2019 • Gilie Gefen, Omer Ben-Porat, Moshe Tennenholtz, Elad Yom-Tov
Here we describe experiments where methods from Mechanism Design were used to elicit a truthful valuation from users for their Internet data and for services to screen people for medical conditions.
no code implementations • 25 May 2019 • Omer Ben-Porat, Fedor Sandomirskiy, Moshe Tennenholtz
In this family, we characterize conditions under which the fairness constraint helps the disadvantaged group.
no code implementations • ICML 2020 • Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz
Recommendation systems often face exploration-exploitation tradeoffs: the system can only learn about the desirability of new options by recommending them to some user.
1 code implementation • 4 May 2019 • Omer Ben-Porat, Moshe Tennenholtz
Despite their centrality in the competition between online companies who offer prediction-based products, the \textit{strategic} use of prediction algorithms remains unexplored.
no code implementations • 2 May 2019 • Reshef Meir, Ofra Amir, Omer Ben-Porat, Tsviel Ben-Shabat, Gal Cohensius, Lirong Xia
Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources.
no code implementations • 14 Jun 2018 • Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz
We consider a game-theoretic model of information retrieval with strategic authors.
no code implementations • 5 Jun 2018 • Omer Ben-Porat, Moshe Tennenholtz
Despite their centrality in the competition between online companies who offer prediction-based products, the strategic use of prediction algorithms remains unexplored.
Computer Science and Game Theory
1 code implementation • NeurIPS 2017 • Omer Ben-Porat, Moshe Tennenholtz
In this work, we initiate the study of strategic predictions in machine learning.