Please note: These are just predictions based on data. Don't take them too seriously! We wish everyone the best of luck with their submissions. 🍀
Enter your OpenReview submission ID to check predictions from our tabular ML models.
This project applies tabular machine learning models to predict ICLR paper acceptance decisions. Unlike LLM-based approaches, we focus on structured feature engineering from review scores, ratings, and metadata.
Historical accuracy of our models on the ICLR 2025 dataset.
CatBoost consistently outperforms baseline Logistic Regression on tabular review data, achieving higher precision in distinguishing borderline Spotlight/Poster decisions.
This project applies tabular machine learning models to predict ICLR paper acceptance decisions. Unlike LLM-based approaches, we focus on structured feature engineering from review scores, ratings, and metadata.
Leverages structured features from review scores, ratings, and paper metadata without relying on text content.
Ensemble of CatBoost, TabPFN, Logistic Regression, and Decision Trees for robust predictions.
Advanced feature engineering including multi-value column expansion, summary statistics, and year-based deltas.
Performance metrics for our tabular ML models on ICLR acceptance prediction.
Download the complete prediction dataset with all model outputs.
📂 Open Results Folder