Publications

Preprints

• Aegis: A Decentralized Expansion Blockchain.
  Yogev Bar-On, Roi Bar-Zur, Omer Ben-Porat, Nimrod Cohen, Ittay Eyal, Matan Sitbon. [arXiv]. (A preliminary version appeared at the Workshop on Scalability & Interoperability of Blockchains of the Advances in Financial Technologies conference (SIB AFT 2024))
• Efficient Individually Rational Recommender System under Stochastic Order.
  Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, and Moshe Tennenholtz.

In Conferences:

• Principal-Agent Reward Shaping in MDPs.
  Omer Ben-Porat, Yishay Mansour, Michal Moshkovitz, Boaz Taitler*.
  The Thirty-Eighth Annual AAAI Conference on Artificial Intelligence (AAAI 2024).[arXiv]
• Personalized Reinforcement Learning with a Budget of Policies.
  Dmitry Ivanov and Omer Ben-Porat.
  The Thirty-Eighth Annual AAAI Conference on Artificial Intelligence (AAAI 2024). [arXiv]
• Learning with Exposure Constraints in Recommendation Systems.
  Omer Ben-Porat and Rotem Torkan.
  The Web Conference 2023 (WWW 23). [arXiv]
• Frustratingly Easy Truth Discovery.
  Reshef Meir, Ofra Amir, Omer Ben-Porat, Tsviel Ben Shabat, Gal Cohensius, Lirong Xia.
  The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 23). [arXiv]
• Modeling Attrition in Recommender Systems with Departing Bandits.
  Omer Ben-Porat*, Lee Cohen*, Liu Leqi*, Zachary Lipton, Yishay Mansour.
  The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 22). [arXiv]
• Corporate Social Responsibility via Multi-Armed Bandits.
  Tom Ron*, Omer Ben-Porat*, and Uri Shalit.
  The Fourth Annual ACM Conference on Fairness, Accountability, and Transparency (ACM FACCT 2021).
• Protecting the Protected Group: Circumventing Harmful Fairness.
  Omer Ben-Porat, Fedor Sandomirskiy and Moshe Tennenholtz.
  The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 21). [arXiv]
• Content Provider Dynamics and Coordination in Recommendation Ecosystems.
  Omer Ben-Porat, Itay Rosenberg and Moshe Tennenholtz.
  Neural Information Processing Systems (NIPS) 2020.
• Fiduciary Bandits.
  Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown and Moshe Tennenholtz.
  The Thirty-seventh International Conference on Machine Learning (ICML 2020). [arXiv] (A preliminary version presented at the “Young” Workshop on Economics and Computation (YoungEC) 19 [Slides, Video])
• Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach.
  Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, and Craig Boutilier.
  The Thirty-seventh International Conference on Machine Learning (ICML 2020).
• Regression Equilibrium.
  Omer Ben-Porat and Moshe Tennenholtz.
  The Twentieth ACM Conference on Economics and Computation (EC 19). [Slides, arXiv, video]
• From Recommendation Systems to Facility Location Games.
  Omer Ben-Porat, Gregory Goren, Itay Rosenberg and Moshe Tennenholtz.
  The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 19). [Slides]
• Convergence of Learning Dynamics in Information Retrieval Games.
  Omer Ben-Porat, Itay Rosenberg and Moshe Tennenholtz.
  The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 19).
• A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers.
  Omer Ben-Porat and Moshe Tennenholtz.
  Neural Information Processing Systems (NIPS) 2018. [arXiv] (A preliminary version presented at the workshop on Game-Theoretic Mechanisms for Data and Information, ICML 2018)
• Shapley Facility Location Games.
  Omer Ben-Porat and Moshe Tennenholtz.
  International Conference on Web and Internet Economics (WINE) 2017. [arXiv]
• Best Response Regression.
  Omer Ben-Porat and Moshe Tennenholtz.
  Neural Information Processing Systems (NIPS) 2017.
• Multi-Unit Facility Location Games.
  Omer Ben-Porat and Moshe Tennenholtz.
  International Conference on Web and Internet Economics (WINE) 2016.

Journals: