AK

- github - google scholar - follow me on X!

I'm a postdoc at Harvard University, where I participate in the SAFR AI lab led by Seth Neel and Salil Vadhan. I obtained my Ph.D. from Boston University under the supervision of Ran Canetti in spring of 2024.

I'm interested in machine learning, AI, and theoretical computer science.

I want to help develop the theory of how machine learning really works—can we predict how ML models will perform/act, before we train (or otherwise do surgery on) them? For instance, some of my recent work contributes to a better understanding when and why multimodal data is useful for ML.

I am now also working on applied research in ML/AI interpretability, such as methods for data attribution and mechanistic interpretability and model editing.

My Ph.D. thesis was in the area of meta-complexity, and in particular, discovered new relationships between the complexity theory of circuit lower bounds and computational learning theory.

blog >>>

berkeley cafe museum >>>





Piet Mondrian, The Red Tree, 1908












Refereed Publications

(ab) indicates author names are listed alphabetically.

  • The Power of Random Features and the Limits of Distribution-Free Gradient Descent. ICML 2025 (to appear)
    Ari Karchmer and Eran Malach.
    ArXiv preprint.

  • On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning. ICML 2024.
    Ari Karchmer.
    "Spotlight" paper (3.5% acceptance rate). ArXiv preprint.

  • Agnostic Membership Query Learning with Nontrivial Savings: New Results and Techniques. ALT 2024.
    Ari Karchmer.
    ArXiv preprint.

  • Distributional PAC-Learning from Nisan's Natural Proofs. ITCS 2024.
    Ari Karchmer.
    Winner of best student paper award. Invited for publication at TheoretiCS. ArXiv preprint.

  • Theoretical Limits of Provable Security Against Model Extraction by Efficient Observational Defenses. SaTML 2023.
    Ari Karchmer.
    IACR ePrint.

  • Covert Learning: How to Learn with an Untrusted Intermediary. TCC 2021. (ab)
    Ran Canetti and Ari Karchmer.
    Invited to Journal of Cryptology. IACR ePrint.

Coming Soon


Workshop Papers


Select Talks

  • "Undetectable Model Stealing with Covert Learning" - Harvard University SAFR AI Lab, Cambridge MA, April '24 Slides
  • "Cryptography and Complexity Theory in the Design and Analysis of ML" - Vector Institue, Toronto CA, April '24 Slides
  • "Learning from Nisan's Natural Proofs" - MIT CIS Seminar, March '24 Slides
  • "Distributional PAC-learning from Nisan's Natural Proofs" - ITCS, Simons Institute, Jan '24 Video
  • "Undetectable Model Stealing and more with Covert Learning" - Google Research MTV, algorithms seminar, Jan '24 Slides
  • "New Approaches to Heuristic PAC-Learning vs. PRFs" - Lower Bounds, Learning, and Average-case Complexity Workshop at Simons Institute, Feb '23 Simons talk
  • "The Limits of Provable Security Against Model Extraction" - Privacy Preserving Machine Learning Workshop at Crypto, Aug '22 PPML talk
  • "Covert Learning: How to Learn with an Untrusted Intermediary" - Charles River Crypto Day at MIT, Nov '21 Crypto day talk

Teaching Fellowships

  • "Responsible AI, Law, Ethics & Society" with Shlomi Hod et al. in Spring 2022
  • "Network Security" with profs Ran Canetti and Sharon Goldberg in Fall 2019
  • "Algebraic Algorithms" with prof Leonid Levin in Fall 2018



Flag Counter