Author

A. Maleki

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Preprint Sep 2026

Instance Optimal Sparse Recovery from Nonlinear Observations: A Unified Framework

This paper develops a unified framework for instance optimal sparse recovery from nonlinear observations. The main ingredient is a signal-dependent restricted approximate invertibility condition (RAIC) of some gradient, which leads to the instance optimality of iterative hard thresholding. Under Gaussian designs, we ap...

Jun-Ren Chen, A. Maleki · 2 citations

#machine learning Preprint Sep 2026

Data Attribution via Sketched Metadifferentiation

Two algorithms, MAGE and SPELL, suited for reconstruction and attribution respectively, are presented, that run on existing metagradient machinery at no extra cost and demonstrate strong performance over existing baselines across training scales and measurement budgets.

Yu-Xi Chen, Hamza Golubovic, Han Tong et al. · 0 citations