21 citations · 42 across the 15 of their papers we have counts for
18 papers
Data Sharing with a Generative AI Competitor
Boaz Taitler, Omer Madmon, Moshe Tennenholtz +1
As GenAI platforms grow, their dependence on content from competing providers, combined with access to alternative data sources, creates new challenges for data-sharing decisions.…
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 with Exposure Constraints in Recommendation Systems
Omer Ben-Porat, Rotem Torkan
Recommendation systems are dynamic economic systems that balance the needs of multiple stakeholders. A recent line of work studies incentives from the content providers' point of v…
Modeling Attrition in Recommender Systems with Departing Bandits
Omer Ben-Porat, Lee Cohen, Liu Leqi +2
Traditionally, when recommender systems are formalized as multi-armed bandits, the policy of the recommender system influences the rewards accrued, but not the length of interactio…
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov, Elliot Creager, Omer Ben-Porat +3
Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In re…
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…
Predicting Strategic Behavior from Free Text
Omer Ben-Porat, Sharon Hirsch, Lital Kuchy +3
The connection between messaging and action is fundamental both to web applications, such as web search and sentiment analysis, and to economics. However, while prominent online ap…
Privacy, Altruism, and Experience: Estimating the Perceived Value of Internet Data for Medical Uses
Gilie Gefen, Omer Ben-Porat, Moshe Tennenholtz +1
People increasingly turn to the Internet when they have a medical condition. The data they create during this process is a valuable source for medical research and for future healt…
Regression Equilibrium
Omer Ben-Porat, Moshe Tennenholtz
Prediction is a well-studied machine learning task, and prediction algorithms are core ingredients in online products and services. Despite their centrality in the competition betw…
Protecting the Protected Group: Circumventing Harmful Fairness
Omer Ben-Porat, Fedor Sandomirskiy, Moshe Tennenholtz
Machine Learning (ML) algorithms shape our lives. Banks use them to determine if we are good borrowers; IT companies delegate them recruitment decisions; police apply ML for crime-…
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…
Frustratingly Easy Truth Discovery
Reshef Meir, Ofra Amir, Omer Ben-Porat +3
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. F…
From Recommendation Systems to Facility Location Games
Omer Ben-Porat, Gregory Goren, Itay Rosenberg +1
Recommendation systems are extremely popular tools for matching users and contents. However, when content providers are strategic, the basic principle of matching users to the clos…
Efficient Crowdsourcing via Proxy Voting
Gal Cohensius, Omer Ben Porat, Reshef Meir +1
Crowdsourcing platforms offer a way to label data by aggregating answers of multiple unqualified workers. We introduce a \textit{simple} and \textit{budget efficient} crowdsourcing…
Convergence of Learning Dynamics in Information Retrieval Games
Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz
We consider a game-theoretic model of information retrieval with strategic authors. We examine two different utility schemes: authors who aim at maximizing exposure and authors who…
Competing Prediction Algorithms
Omer Ben-Porat, Moshe Tennenholtz
Prediction is a well-studied machine learning task, and prediction algorithms are core ingredients in online products and services. Despite their centrality in the competition betw…
A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers
Omer Ben-Porat, Moshe Tennenholtz
We introduce a game-theoretic approach to the study of recommendation systems with strategic content providers. Such systems should be fair and stable. Showing that traditional app…
Shapley Facility Location Games
Omer Ben-Porat, Moshe Tennenholtz
Facility location games have been a topic of major interest in economics, operations research and computer science, starting from the seminal work by Hotelling. Spatial facility lo…