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From the 1 of 8 linked papers with an AI index.

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20242026
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cs.LG2026

Haiku to Opus in Just 10 bits: LLMs Unlock Large Compression Gains

Roy Rinberg, Annabelle Michael Carrell, Simon Henniger +2

We study the compression of LLM-generated text across lossless and lossy regimes, characterizing a compression-compute frontier where more compression is possible at the cost of mo…

cs.LG2026

Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning

Roy Rinberg, Ilia Shumailov, Vikrant Singhal +2

Differential privacy (DP) is obtained by randomizing a data analysis algorithm, which necessarily introduces a tradeoff between its utility and privacy. Many DP mechanisms are buil…

cs.LG2026

Easy Data Unlearning Bench

Roy Rinberg, Pol Puigdemont, Martin Pawelczyk +1

Evaluating machine unlearning methods remains technically challenging, with recent benchmarks requiring complex setups and significant engineering overhead. We introduce a unified…

cs.LG2025

DiFR: Inference Verification Despite Nondeterminism

Adam Karvonen, Daniel Reuter, Roy Rinberg +3

As demand for LLM inference grows, it is becoming increasingly important that providers and their customers can verify that inference processes are performed correctly, without err…

cs.LG2024

Attribute-to-Delete: Machine Unlearning via Datamodel Matching

Kristian Georgiev, Roy Rinberg, Sung Min Park +4

Machine unlearning -- efficiently removing the effect of a small "forget set" of training data on a pre-trained machine learning model -- has recently attracted significant researc…