Yahav Bechavod (original) (raw)
I am on the 2025-2026 academic job market!
I am a Postdoctoral Researcher at the School of Engineering and Applied Science at the University of Pennsylvania, working with Prof. Aaron Roth and Prof. Michael Kearns. I am also part of the Penn CS Theory Group.
Prior to joining Penn, I earned my PhD from the School of Computer Science and Engineering at the Hebrew University of Jerusalem, during which I was also an Apple Scholar in AI/ML. My research interests are primarily in algorithms, machine learning, and game theory, and specifically in the areas of fairness in machine learning, online learning, and learning in the presence of strategic behavior.
I am honored to be the recipient of several awards and fellowships, including the Israeli Council for Higher Education Postdoctoral Fellowship, the Apple Scholars in AI/ML PhD Fellowship, and the Charles Clore Foundation PhD Fellowship. At Penn, I am also an AI x Science Fellow at the School of Arts & Sciences.
Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
[arXiv]
Yahav Bechavod, Jiuyao Lu, Aaron Roth
Manuscript in submission, 2025
Online Omniprediction with Long-Term Constraints
[arXiv]
Yahav Bechavod, Jiuyao Lu, Aaron Roth
Manuscript in submission, 2025. Preliminary version accepted at NeurIPS 2025 Workshop on Constrained Optimization for Machine Learning
Monotone Individual Fairness
[arXiv] [Conference Version]
Yahav Bechavod
In Proc. of the 41st International Conference on Machine Learning (ICML 2024)
Individually Fair Learning with One-Sided Feedback
[arXiv] [Conference Version] [Slides] [[Poster]](files/ind fair poster.pdf)
Yahav Bechavod, Aaron Roth
In Proc. of the 40th International Conference on Machine Learning (ICML 2023)
Information Discrepancy in Strategic Learning
[arXiv] [Conference Version] [Slides] [[Poster]](files/info-disc poster.pdf)
Yahav Bechavod, Chara Podimata, Steven Wu, Juba Ziani
In Proc. of the 39th International Conference on Machine Learning (ICML 2022)
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
[arXiv] [Conference Version] [Video] [Slides] [[Poster]](files/gaming poster.pdf)
Yahav Bechavod, Katrina Ligett, Steven Wu, Juba Ziani
In Proc. of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)
Metric-Free Individual Fairness in Online Learning
[arXiv] [Conference Version] [Talk at HUJI ML Seminar] [Slides] [[Poster]](files/metric-free poster.pdf)
Yahav Bechavod, Christopher Jung, Steven Wu
In Proc. of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020)
Selected for Oral Presentation (top 1.1% of submissions)
Equal Opportunity in Online Classification with Partial Feedback
[arXiv] [Conference Version] [Talk at Simons] [Slides] [[Poster]](files/equalOpp poster.pdf)
Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Wu
In Proc. of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
Recent & Selected Presentations
Presentation slides are available upon request.
- Individual Fairness in Online Classification
- UPenn CS Theory Seminar. February 2023.
- Theory of Computation for Fairness (A Simons Foundation Collaboration) Online Seminar. February 2023. Video here.
- Individually Fair Learning with One-Sided Feedback
- ICML Workshop: Responsible Decision Making in Dynamic Environments. Contributed Talk. July 2022. Video here.
- Information Discrepancy in Strategic Learning
- ICML Spotlight Presentation. July 2022. Video here.
- Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
- AISTATS Poster Presentation. April 2021.
- Metric-Free Individual Fairness in Online Learning
- KLA PhD Awards Ceremony. July 2021.
- Apple Machine Learning Speaker Series. June 2021.
- Symposium on the Foundations of Responsible Computing (FORC). June 2021. Video here.
- Machine & Deep Learning Israel. December 2020.
- NeurIPS Oral Presentation. December 2020. Video here.
- NeurIPS Poster Presentation. December 2020.
- Hebrew University Avdanced Seminar in Machine Learning. November 2020. Video here.
- A Brief Introduction to Algorithmic Fairness
- Hebrew University Federmann Center for the Study of Rationality. February 2020.
- Special Presentation for Google’s APM Program. June 2018.
- Equal Opportunity in Online Classification with Partial Feedback
- NeurIPS Poster Presentation. December 2019.
- Simons Institute Workshop on Developments in Research on Fairness. July 2019. Video here.
- Adversarial Bandits - Regret Bounds and Analysis
- Hebrew University Advanced Topics in Machine Learning Seminar. May 2018.
Workshops & Panels
- NeurIPS 2021 Workshop: Learning and Decision-Making with Strategic Feedback (StratML)
Virtual. December 2021. Video here. - FORC 2021 Panel: Translation of Fair Learning to Practice
Virtual. June 2021. Link here.
Professional Service
- Journal Refereeing
- Journal of Machine Learning Research (JMLR)
- Conference Program Committee
- International Conference on Machine Learning (ICML): 2022, 2023, 2024, 2025
- Conference on Neural Information Processing Systems (NeurIPS): 2021, 2022, 2023
- ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT): 2021
- Conference Auxilary Reviewer
- Innovations in Theoretical Computer Science (ITCS): 2023
- AAAI Conference on Artificial Intelligence, Ethics and Social Intelligence (AIES): 2019
- Ethical Reviewer
- Conference on Neural Information Processing Systems (NeurIPS): 2021, 2022, 2023
- Workshop Organizing
- NeurIPS 2021 Workshop on Learning and Decision-Making with Strategic Feedback. View here.
Awards
Contact Me
Office: 3401 Walnut Street, Room 409B.
Email: yahav [at] seas [dot] upenn [dot] edu.