Author

Anuj Apte

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Preprint Sep 2026

Cycle Codes and Decoded Quantum Interferometry

Decoded Quantum Interferometry (DQI) reduces optimization problems with two-variable constraints to decoding cycle codes. For one such problem, namely MaxCut, prior work showed that DQI achieves a nontrivial satisfaction fraction guarantee only on linear-girth graphs, for which MaxCut is classically easy. However, thes...

Anuj Apte, Shouvanik Chakrabarti, An-Di Gu et al. · 0 citations

#artificial intelligence May 2026

Anytime Training with Schedule-Free Spectral Optimization

SF-NorMuon, a schedule-free spectral optimizer that closes the performance gap with tuned AdamW baselines, makes horizon-free optimization more practical, taking a step towards truly open-ended, continual learning.

Anuj Apte, Pranav Deshpande, Niraj Kumar et al. · arXiv.org · 4 citations

Jul 2026

Scale Weight Decay and Train Better

The discovery of scaling laws has motivated training neural networks on ever increasing quantities of data. This is typically done with a constant decoupled weight decay which causes the network weights to shrink steadily over the course of training. Taking inspiration from the Robbins--Monro conditions, we propose to...

Anuj Apte · arXiv.org · 0 citations