Author

Semih Cayci

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#machine learning Preprint Sep 2026

A Comparative Analysis of Attention versus State-Space Models for In-Context Learning

Transformers and state-space models (SSMs) are two prominent sequential learning architectures, yet their comparison remains largely empirical and existing theoretical analyses are typically task-specific or architecturally restricted. In this paper, we develop belief geometry, a unified analytical framework for compar...

Enes Arda, Semih Cayci, Atilla Eryilmaz · 0 citations

#machine learning Open access Mar 2024

Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients

This work studies Fisher-Rao gradient flows of linear programs more generally and shows linear convergence with a rate that depends on the geometry of the linear program, and shows sublinear convergence for perturbed Fisher-Rao gradient flows and natural gradient flows up to an approximation error.

Johannes Müller, Semih Cayci, Guido Montúfar · SIAM Journal on Optimization · 6 citations