Ömer Faruk Akgül — accepted AI-conference papers

2026

Tina: Tiny Reasoning Models via LoRA

Shangshang Wang, Julian Asilis, Ömer Faruk Akgül, Enes Burak Bilgin, Ollie Liu, Willie Neiswanger

ICLR 2026poster

How cost-effectively can strong reasoning abilities be achieved in language models? Driven by this question, we present Tina, a family of tiny reasoning models achieved with high cost-efficiency. Tina shows that substantial reasoning performance can be developed using only minimal resources, by appl…

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2025

Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information

Ömer Faruk Akgül, Rajgopal Kannan, Viktor Prasanna

UAI 2025

Uncertainty quantification is essential for reliable federated graph learning, yet existing methods struggle with decentralized and heterogeneous data. In this work, we first extend Conformal Prediction (CP), a well-established method for uncertainty quantification, to federated graph learning, form

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