AK

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I'm a member of the Machine Learning Research team at Morgan Stanley. Previously, I was a postdoc at Harvard University, where I was hosted by Seth Neel. 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've worked on research problems in a variety of areas, including:

  • Machine Learning theory, especially complexity separations (e.g., random features vs. gradient descent, multimodal vs unimodal learning)
  • Machine Learning interpretability, including data attribution and verifiability of attribution
  • Computational Learning and Complexity theory, especially meta-complexity and the relationship between circuit lower bounds and computational learning theory (my thesis)
  • Cryptography and Machine Learning security, including the model stealing problem and "Covert Learning"
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.

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Refereed Publications

  1. The Power of Random Features and the Limits of Distribution Free Gradient Descent. ICML 2025
    Ari Karchmer and Eran Malach.
    ArXiv preprint.

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

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

  4. 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.

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

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


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



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