Jonathan Shafer

I am a researcher. I work in theoretical computer science, and I'm especially fascinated with computational and statistical learning theory.

I am currently a postdoctoral associate at MIT, hosted by Vinod Vaikuntanathan. Previously, I earned a PhD from UC Berkeley advised by Shafi Goldwasser, and an MSc from Tel Aviv University advised by Amir Shpilka and Amir Yehudayoff. I completed my undergraduate studies at the Lautman Interdisciplinary Program.


& Preprints

  1. Fantastic Generalization Measures are Nowhere to be Found

    With Michael Gastpar, Ido Nachum and Thomas Weinberger

    • Conference version: 12th International Conference on Learning Representations (ICLR 2024)
    • Video, slides: Ido presenting at ICLR, May 8th 2024
    • Poster: designed by Thomas for ICLR
  2. A Trichotomy for Transductive Online Learning

    With Steve Hanneke and Shay Moran

    • Conference version: 37th Annual Conference on Neural Information Processing System (NeurIPS 2023)
    • Video, slides: Jonathan presenting at NeurIPS, December 10th 2023
  3. The Bayesian Stability Zoo

    With Shay Moran and Hilla Schefler

  4. Fine-Grained Distribution-Dependent Learning Curves

    With Olivier Bousquet, Steve Hanneke, Shay Moran and Ilya Tolstikhin

  5. PAC Verification of Statistical Algorithms

    With Saachi Mutreja

  6. Interactive Proofs for Verifying Machine Learning

    With Shafi Goldwasser, Guy Rothblum and Amir Yehudayoff

    • Video: Shafi presenting as part of her invited talk at NeurIPS, December 8th 2020
    • Conference version: 12th Innovations in Theoretical Computer Science Conference (ITCS 2021)
    • Video, slides: Jonathan presenting at ITCS, January 8th 2021
    • Also presented at the Charles River Crypto Day, July 31st 2020; the Theory Seminar at Weizmann Institute of Science, January 19th 2020; and the Theory Seminar at Tel Aviv University, January 16th 2020
  7. A Direct Sum Result for the Information Complexity of Learning

    With Ido Nachum and Amir Yehudayoff

  8. Learners that Use Little Information

    With Raef Bassily, Shay Moran, Ido Nachum, and Amir Yehudayoff

    • Conference version: 29th International Conference on Algorithmic Learning Theory (ALT 2018)


  1. Computational Learning Theory


    UC Berkeley, CS 294-220, Spring 2021

  2. Computability and Complexity

    Teaching Assistant

    UC Berkeley, CS 172, Spring 2019

    • Notes: Discussions delivered and scribed by Jonathan
  3. Algorithms for Computational Linguistics


    Tel Aviv University, 0627-2235-01, Spring 2017
    Replaced a professor that was on sabbatical

    • Notes: Lectures delivered and scribed by Jonathan (Hebrew)
  4. Computational Linguistics for Beginners

    Teaching Assistant

    Tel Aviv University, 0627-2221-01, Spring 2016

  5. Advanced Computational Linguistics

    Teaching Assistant

    Tel Aviv University, 0627-4090-02, Fall 2015

    • Notes: Discussions delivered by Jonathan and scribed by Noa Peled (Hebrew)