Search Results for author: Omer Ben-Porat

Found 16 papers, 5 papers with code

Personalized Reinforcement Learning with a Budget of Policies

1 code implementation12 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.

Autonomous Driving Recommendation Systems +1

Principal-Agent Reward Shaping in MDPs

1 code implementation30 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.

Learning with Exposure Constraints in Recommendation Systems

no code implementations2 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.

Recommendation Systems

Modeling Attrition in Recommender Systems with Departing Bandits

no code implementations25 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.

Multi-Armed Bandits Recommendation Systems

Content Provider Dynamics and Coordination in Recommendation Ecosystems

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).

Combinatorial Optimization Recommendation Systems

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

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.

Fairness Recommendation Systems

Learning under Invariable Bayesian Safety

no code implementations8 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.

Predicting Strategic Behavior from Free Text

1 code implementation6 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.

Sentiment Analysis Transductive Learning

Privacy, Altruism, and Experience: Estimating the Perceived Value of Internet Data for Medical Uses

no code implementations20 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.

Protecting the Protected Group: Circumventing Harmful Fairness

no code implementations25 May 2019 Omer Ben-Porat, Fedor Sandomirskiy, Moshe Tennenholtz

In this family, we characterize conditions under which the fairness constraint helps the disadvantaged group.

Crime Prediction Fairness

Fiduciary Bandits

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.

Recommendation Systems

Regression Equilibrium

1 code implementation4 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.

PAC learning regression

Frustratingly Easy Truth Discovery

no code implementations2 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.

Competing Prediction Algorithms

no code implementations5 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

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