Yigit Ihlamur

According to our database, Yigit Ihlamur authored at least 15 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of five.

2026

Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarm.

Nathan Li, Aikins Laryea, Yigit Ihlamur

CoRR, April, 2026

CoFEE: Reasoning Control for LLM-Based Feature Discovery.

Maximilian Westermann, Ben Griffin, Aaron Ontoyin Yin, Zakari Salifu, Yagiz Ihlamur, Kelvin Amoaba, Joseph Ternasky, Fuat Alican, Yigit Ihlamur

CoRR, April, 2026

From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code.

Anirudh Jaidev Mahesh, Ben Griffin, Fuat Alican, Joseph Ternasky, Zakari Salifu, Kelvin Amoaba, Yagiz Ihlamur, Aaron Ontoyin Yin, Aikins Laryea, Afriyie Kwesi Samuel, Yigit Ihlamur

CoRR, March, 2026

Probabilistic Modeling of Venture Capital Portfolio Outliers.

Kensei Sakamoto, Hasan Ugur Koyluoglu, Fuat Alican, Yigit Ihlamur

CoRR, February, 2026

2025

LLM-AR: LLM-powered Automated Reasoning Framework.

Rick Chen, Joseph Ternasky, Aaron Ontoyin Yin, Xianling Mu, Fuat Alican, Yigit Ihlamur

CoRR, October, 2025

VCBench: Benchmarking LLMs in Venture Capital.

Rick Chen, Joseph Ternasky, Afriyie Samuel Kwesi, Ben Griffin, Aaron Ontoyin Yin, Zakari Salifu, Kelvin Amoaba, Xianling Mu, Fuat Alican, Yigit Ihlamur

CoRR, September, 2025

From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital.

Mihir Kumar, Aaron Ontoyin Yin, Zakari Salifu, Kelvin Amoaba, Afriyie Kwesi Samuel, Fuat Alican, Yigit Ihlamur

CoRR, September, 2025

Random Rule Forest (RRF): Interpretable Ensembles of LLM-Generated Questions for Predicting Startup Success.

Ben Griffin, Joseph Ternasky, Fuat Alican, Yigit Ihlamur

CoRR, May, 2025

Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning.

Xianling Mu, Joseph Ternasky, Fuat Alican, Yigit Ihlamur

CoRR, May, 2025

Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Framework.

Jack Preuveneers, Joseph Ternasky, Fuat Alican, Yigit Ihlamur

CoRR, April, 2025

GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification.

Te Pei, Fuat Alican, Aaron Ontoyin Yin, Yigit Ihlamur

CoRR, January, 2025

2024

GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees.

Sichao Xiong, Yigit Ihlamur, Fuat Alican, Aaron Ontoyin Yin

CoRR, 2024

Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques.

Ekin Ozince, Yigit Ihlamur

CoRR, 2024

An Automated Startup Evaluation Pipeline: Startup Success Forecasting Framework (SSFF).

Xisen Wang, Yigit Ihlamur

CoRR, 2024

2023

Founder-GPT: Self-play to evaluate the Founder-Idea fit.

Sichao Xiong, Yigit Ihlamur

CoRR, 2023