Author

Sharan Sahu

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#machine learning May 2026

Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization

Borders reveal a noise--drift tradeoff: in noise-dominated regimes, first-moment averaging and adaptive preconditioning can yield favorable upper guarantees, whereas in drift-dominated regimes, stale first-moment information and preconditioning can enlarge Adam's tracking guarantee, potentially allowing vanilla SGD to...

Sharan Sahu, Abir Sarkar, Cameron J. Hogan et al. · arXiv.org · 0 citations

#machine learning Preprint Aug 2026

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models

HOPPER is introduced, an end-to-end learnable extension of LGSM that learns how hop sequences should be extracted before processing by a modern state-space model, and demonstrates that learnable sequence extraction is a flexible and effective framework for long-range graph representation learning across synthetic, phys...

Isuru Herath, Arin Gopakumar, Sharan Sahu · 0 citations