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Robin Yadav

3 accepted papers

2025

Local Curvature Descent: Squeezing More Curvature out of Standard and Polyak Gradient Descent

NeurIPS 2025poster

We contribute to the growing body of knowledge on more powerful and adaptive stepsizes for convex optimization, empowered by local curvature information. We do not go the route of fully-fledged second-order methods, which require the expensive computation of the Hessian. Instead, our key observation…

Cited by 0SourceScholar
2025

RETRO SYNFLOW: Discrete Flow-Matching for Accurate and Diverse Single-Step Retrosynthesis

NeurIPS 2025poster

A fundamental challenge in organic chemistry is identifying and predicting the sequence of reactions that synthesize a desired target molecule. Due to the combinatorial nature of the chemical search space, single-step reactant prediction—i.e., single-step retrosynthesis—remains difficult, even for s…

Cited by 0SourceScholar
2024

Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models

NeurIPS 2024spotlight

Adam has been shown to outperform gradient descent on large language models by a larger margin than on other tasks, but it is unclear why. We show that a key factor in this performance gap is the heavy-tailed class imbalance found in language tasks. When trained with gradient descent, the loss of in…

Cited by 32SourcePDFScholar