Welcome!


Yahav Bechavod, The Hebrew University
   

Yahav Bechavod
yahav [at] seas [dot] upenn [dot] edu


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I am a Postdoctoral Researcher in the School of Engineering and Applied Science at the University of Pennsylvania, working with Aaron Roth. Prior to joining Penn, I was a PhD student at the School of Computer Science and Engineering at the Hebrew University of Jerusalem and 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, on which I co-organized a NeurIPS 2021 Workshop on Learning and Decision-Making with Strategic Feedback. 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.

Recent Updates:

[December 2022] I conducted an interview with Juba Ziani for the latest installment of ACM SIGecom Exchanges.
[June 2022] My paper with Aaron will be featured at two ICML 2022 workshops: Responsible Decision Making in Dynamic Environments (selected as contributed talk) and New Frontiers in Adversarial Machine Learning.
[May 2022] My paper with Chara, Steven, and Juba has been accepted at ICML 2022!

Recent & Selected Publications


See my Google Scholar profile for all publications.


Individually Fair Learning with One-Sided Feedback
[arXiv]
Yahav Bechavod, Aaron Roth
Manuscript


Information Discrepancy in Strategic Learning
[arXiv] [Conference Version] [Slides] [Poster]
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]
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]
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]
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.


  • 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 Reviewer
    • Journal of Machine Learning Research (JMLR), 2021
  • Conference Reviewer
    • International Conference on Machine Learning (ICML) 2022
    • Conference on Neural Information Processing Systems (NeurIPS): 2021, 2022
    • ACM Conference on Fairness, Accountability, and Transparency (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
  • Workshop Co-Organizer
    • NeurIPS 2021 Workshop on Learning and Decision-Making with Strategic Feedback
  • Co-Manager
    • Theory of Computation for Fairness (A Simons Foundation Research Collaboration) blog on Algorithmic Fairness, 2021

About Me


Yahav Bechavod, The Hebrew University

Yahav Bechavod, The Hebrew University

Yahav Bechavod, The Hebrew University

In 2019, I had the privilege of spending an amazing summer at Berkeley, California, visiting the Simons Institute as a participant in the Summer Cluster: Algorithmic Fairness program.

In the Fall of 2017, I was fortunate to visit the University of Pennsylvania at the invitation of Aaron Roth and Michael Kearns.

I received both my MSc (Computer Science, Summa Cum Laude) and BSc (Computer Science and Mathematics) from the Hebrew University.

In my spare time, I shoot select 3-pointers on select basketball courts, speak fluent Arabic and not-so-fluent Japanese, blog about CS and non-CS stuff, and travel to cool places on pandemic-less times.

Contact Me


Office: 3401 Walnut Street, Room 464C.

Email: yahav [at] seas [dot] upenn [dot] edu.