:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Pei, Te, Alican, Fuat, Yin, Aaron Ontoyin, Ihlamur, Yigit
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2501.13743
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees
by: Xiong, Sichao, et al.
Published: (2024)

LLM-AR: LLM-powered Automated Reasoning Framework
by: Chen, Rick, et al.
Published: (2025)

Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning
by: Mu, Xianling, et al.
Published: (2025)

From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
by: Kumar, Mihir, et al.
Published: (2025)

CoFEE: Reasoning Control for LLM-Based Feature Discovery
by: Westermann, Maximilian, et al.
Published: (2026)

From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code
by: Mahesh, Anirudh Jaidev, et al.
Published: (2026)

SSFF: Investigating LLM Predictive Capabilities for Startup Success through a Multi-Agent Framework with Enhanced Explainability and Performance
by: Wang, Xisen, et al.
Published: (2024)

Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Framework
by: Preuveneers, Jack, et al.
Published: (2025)

Random Rule Forest (RRF): Interpretable Ensembles of LLM-Generated Questions for Predicting Startup Success
by: Griffin, Ben, et al.
Published: (2025)

Probabilistic Modeling of Venture Capital Portfolio Outliers
by: Sakamoto, Kensei, et al.
Published: (2026)

Beyond Picking Winners: Correlation-Driven Tail Risk in Venture Capital Portfolio Construction
by: Liang, Yunqi, et al.
Published: (2026)

VCBench: Benchmarking LLMs in Venture Capital
by: Chen, Rick, et al.
Published: (2025)

When Career Data Runs Out: Structured Feature Engineering and Signal Limits for Founder Success Prediction
by: Ihlamur, Yagiz
Published: (2026)

Explainable Artificial Intelligence for Medical Applications: A Review
by: Sun, Qiyang, et al.
Published: (2024)

Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques
by: Ozince, Ekin, et al.
Published: (2024)

Hierarchical Text Classification Using Black Box Large Language Models
by: Yoshimura, Kosuke, et al.
Published: (2025)

Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications
by: Su, Guangxin, et al.
Published: (2025)

EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation
by: Kim, Jonathan W., et al.
Published: (2024)

Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification
by: Levatić, Jurica, et al.
Published: (2022)

A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem
by: Daysalilar, Mertcan, et al.
Published: (2026)

Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control
by: Naagarajan, Ramesh Arvind, et al.
Published: (2026)

A Neural Network Framework for Discovering Closed-form Solutions to Quadratic Programs with Linear Constraints
by: Beylunioglu, Fuat Can, et al.
Published: (2025)

LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting
by: Li, Te, et al.
Published: (2024)

Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach
by: Gurses, Yigit, et al.
Published: (2023)

PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals
by: Ihlamur, Yagiz, et al.
Published: (2026)

Hierarchical Job Classification with Similarity Graph Integration
by: Kabir, Md Ahsanul, et al.
Published: (2025)

ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search
by: Yilmaz, Alican, et al.
Published: (2025)

Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation
by: Mertan, Alican, et al.
Published: (2024)

Controller Distillation Reduces Fragile Brain-Body Co-Adaptation and Enables Migrations in MAP-Elites
by: Mertan, Alican, et al.
Published: (2025)

Utilizing Large Language Models for Machine Learning Explainability
by: Vassiliades, Alexandros, et al.
Published: (2025)

Explainable Evidential Clustering
by: de Souza, Victor F. Lopes, et al.
Published: (2025)

In-Context Clustering with Large Language Models
by: Wang, Ying, et al.
Published: (2025)

UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking
by: Yesilkaynak, V. Bugra, et al.
Published: (2025)

Expert with Clustering: Hierarchical Online Preference Learning Framework
by: Zhou, Tianyue, et al.
Published: (2024)

A Hierarchical Neural Framework for Classification and its Explanation in Large Unstructured Legal Documents
by: Prasad, Nishchal, et al.
Published: (2023)

ArabianGPT: Native Arabic GPT-based Large Language Model
by: Koubaa, Anis, et al.
Published: (2024)

HiGPT: Heterogeneous Graph Language Model
by: Tang, Jiabin, et al.
Published: (2024)

Fully Explainable Classification Models Using Hyperblocks
by: Snyder, Austin, et al.
Published: (2025)

Two-Stage Hierarchical and Explainable Feature Selection Framework for Dimensionality Reduction in Sleep Staging
by: Deng, Yangfan, et al.
Published: (2024)

Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples
by: Li, Yicen, et al.
Published: (2026)