Saved in:
| Main Author: | Ihlamur, Yagiz |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.00339 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals
by: Ihlamur, Yagiz, et al.
Published: (2026)
by: Ihlamur, Yagiz, et al.
Published: (2026)
CoFEE: Reasoning Control for LLM-Based Feature Discovery
by: Westermann, Maximilian, et al.
Published: (2026)
by: Westermann, Maximilian, et al.
Published: (2026)
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)
by: Kumar, Mihir, et al.
Published: (2025)
Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning
by: Mu, Xianling, et al.
Published: (2025)
by: Mu, Xianling, et al.
Published: (2025)
From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code
by: Mahesh, Anirudh Jaidev, et al.
Published: (2026)
by: Mahesh, Anirudh Jaidev, et al.
Published: (2026)
Random Rule Forest (RRF): Interpretable Ensembles of LLM-Generated Questions for Predicting Startup Success
by: Griffin, Ben, et al.
Published: (2025)
by: Griffin, Ben, et al.
Published: (2025)
GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification
by: Pei, Te, et al.
Published: (2025)
by: Pei, Te, et al.
Published: (2025)
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization
by: Li, Xiner, et al.
Published: (2023)
by: Li, Xiner, et al.
Published: (2023)
Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques
by: Ozince, Ekin, et al.
Published: (2024)
by: Ozince, Ekin, et al.
Published: (2024)
Feature Engineering on LMS Data to Optimize Student Performance Prediction
by: Hubbard, Keith, et al.
Published: (2025)
by: Hubbard, Keith, et al.
Published: (2025)
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
by: Si, Jacob, et al.
Published: (2024)
by: Si, Jacob, et al.
Published: (2024)
Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data
by: Zou, Xin, et al.
Published: (2024)
by: Zou, Xin, et al.
Published: (2024)
When More Data Doesn't Help: Limits of Adaptation in Multitask Learning
by: Hanneke, Steve, et al.
Published: (2026)
by: Hanneke, Steve, et al.
Published: (2026)
Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction
by: Zuo, Shuozhi, et al.
Published: (2026)
by: Zuo, Shuozhi, et al.
Published: (2026)
Explainable AI Integrated Feature Engineering for Wildfire Prediction
by: Fan, Di, et al.
Published: (2024)
by: Fan, Di, et al.
Published: (2024)
Feature Protection For Out-of-distribution Generalization
by: Tan, Lu, et al.
Published: (2024)
by: Tan, Lu, et al.
Published: (2024)
GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees
by: Xiong, Sichao, et al.
Published: (2024)
by: Xiong, Sichao, et al.
Published: (2024)
SE-MLP Model for Predicting Prior Acceleration Features in Penetration Signals
by: Li, Yankang, et al.
Published: (2025)
by: Li, Yankang, et al.
Published: (2025)
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data
by: Dablain, Damien A., et al.
Published: (2024)
by: Dablain, Damien A., et al.
Published: (2024)
EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs
by: Bensmail, Chama
Published: (2025)
by: Bensmail, Chama
Published: (2025)
When LRP Diverges from Leave-One-Out in Transformers
by: You, Weiqiu, et al.
Published: (2025)
by: You, Weiqiu, et al.
Published: (2025)
How Usable is Automated Feature Engineering for Tabular Data?
by: Schäfer, Bastian, et al.
Published: (2025)
by: Schäfer, Bastian, et al.
Published: (2025)
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
by: Ahmad, Tamim El, et al.
Published: (2023)
by: Ahmad, Tamim El, et al.
Published: (2023)
Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
by: Haroon, Adam, et al.
Published: (2026)
by: Haroon, Adam, et al.
Published: (2026)
In-Run Data Shapley for Adam Optimizer
by: Ding, Meng, et al.
Published: (2026)
by: Ding, Meng, et al.
Published: (2026)
Data Shapley in One Training Run
by: Wang, Jiachen T., et al.
Published: (2024)
by: Wang, Jiachen T., et al.
Published: (2024)
LLM-AR: LLM-powered Automated Reasoning Framework
by: Chen, Rick, et al.
Published: (2025)
by: Chen, Rick, et al.
Published: (2025)
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection
by: Wang, Weixing, et al.
Published: (2024)
by: Wang, Weixing, et al.
Published: (2024)
Filtering with Confidence: When Data Augmentation Meets Conformal Prediction
by: Wu, Zixuan, et al.
Published: (2025)
by: Wu, Zixuan, et al.
Published: (2025)
Out-of-Distribution Generalization in Climate-Aware Yield Prediction with Earth Observation Data
by: Chakravarty, Aditya
Published: (2025)
by: Chakravarty, Aditya
Published: (2025)
When and How Does In-Distribution Label Help Out-of-Distribution Detection?
by: Du, Xuefeng, et al.
Published: (2024)
by: Du, Xuefeng, et al.
Published: (2024)
SCOPE-FE: Structured Control of Operator and Pairwise Exploration for Feature Engineering
by: Park, Minhee, et al.
Published: (2026)
by: Park, Minhee, et al.
Published: (2026)
Spurious Feature Diversification Improves Out-of-distribution Generalization
by: Lin, Yong, et al.
Published: (2023)
by: Lin, Yong, et al.
Published: (2023)
AI-Driven Early Warning Systems for Student Success: Discovering Static Feature Dominance in Temporal Prediction Models
by: Kaushal, Vaarunay, et al.
Published: (2025)
by: Kaushal, Vaarunay, et al.
Published: (2025)
Signals of Success and Struggle: Early Prediction and Physiological Signatures of Human Performance across Task Complexity
by: Cao, Yufei, et al.
Published: (2026)
by: Cao, Yufei, et al.
Published: (2026)
When Learning Rates Go Wrong: Early Structural Signals in PPO Actor-Critic
by: Fernández-Hernández, Alberto, et al.
Published: (2026)
by: Fernández-Hernández, Alberto, et al.
Published: (2026)
When do World Models Successfully Learn Dynamical Systems?
by: Ross, Edmund, et al.
Published: (2025)
by: Ross, Edmund, et al.
Published: (2025)
Domain Feature Collapse: Implications for Out-of-Distribution Detection and Solutions
by: Yang, Hong, et al.
Published: (2025)
by: Yang, Hong, et al.
Published: (2025)
Run LoRA Run: Faster and Lighter LoRA Implementations
by: Cherniuk, Daria, et al.
Published: (2023)
by: Cherniuk, Daria, et al.
Published: (2023)
Predicting Language Models' Success at Zero-Shot Probabilistic Prediction
by: Ren, Kevin, et al.
Published: (2025)
by: Ren, Kevin, et al.
Published: (2025)
Similar Items
-
PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals
by: Ihlamur, Yagiz, et al.
Published: (2026) -
CoFEE: Reasoning Control for LLM-Based Feature Discovery
by: Westermann, Maximilian, et al.
Published: (2026) -
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) -
Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning
by: Mu, Xianling, et al.
Published: (2025) -
From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code
by: Mahesh, Anirudh Jaidev, et al.
Published: (2026)