TabICL scales in-context learning to large tabular data via column-then-row attention for row embeddings followed by a transformer, matching TabPFNv2 speed and performance while outperforming it and CatBoost on datasets over 10K samples.
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CatBoost: gradient boosting with categorical features support
12 Pith papers cite this work. Polarity classification is still indexing.
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abstract
In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of learning algorithm and a CPU implementation of scoring algorithm, which are significantly faster than other gradient boosting libraries on ensembles of similar sizes.
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use method 2representative citing papers
Smart-SIEM adds context-aware ML profiling to Wazuh SIEM, lifting binary attack detection F1 to 0.967 and six-class categorization to 0.914 while recovering from concept drift via retraining.
KNN imputation gives highest photo-z accuracy under ideal random missingness with complete training data, while SAITS is more robust for incomplete training sets and realistic mixed missingness patterns in CSST data.
AutoGluon-Tabular achieves superior accuracy on tabular classification and regression by multi-layer model ensembling and stacking, outperforming other AutoML frameworks on 50 benchmarks and Kaggle competitions.
The synthetic prior for tabular foundation models covers only a narrow part of real table distributions, but this mismatch does not degrade model generalization.
Pith review generated a malformed one-line summary.
A multimodal neural network trained on MPA-JHU references produces SFR, stellar mass, and metallicity estimates for 547 million low-redshift galaxies in DESI LS DR10.
Machine learning models trained on Bangladeshi community data achieve 89-90% balanced accuracy for early CKD detection using few accessible features, outperforming traditional screening tools and generalizing across external datasets from India, UAE, and Bangladesh.
Machine learning models achieve NMAD 0.036 and 5.6% outliers for quasar photometric redshifts, identifying 185 high-probability pair candidates in MGQPC with 20 spectroscopically confirmed as physical pairs.
Donor-aware benchmarks show AUROCs up to 0.978 for IBD classification from scRNA-seq using CLR cell-type compositions and GatedStructuralCFN embeddings, with compartment stratification improving both performance and feature stability.
Hybrid predictive modeling of crash data identifies non-use of restraints as the primary risk factor for severe injury in collisions involving trees.
Generalized ML models trained on past sales data forecast demand for new fashion items from their attributes, with experiments across neural architectures and loss functions showing robust performance.
citing papers explorer
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TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
TabICL scales in-context learning to large tabular data via column-then-row attention for row embeddings followed by a transformer, matching TabPFNv2 speed and performance while outperforming it and CatBoost on datasets over 10K samples.
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Context-Aware Web Attack Detection in Open-Source SIEM Systems via MITRE ATT&CK-Enriched Behavioral Profiling
Smart-SIEM adds context-aware ML profiling to Wazuh SIEM, lifting binary attack detection F1 to 0.967 and six-class categorization to 0.914 while recovering from concept drift via retraining.
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Comparative analysis of missing data imputation methods for CSST survey: Impact on photometric redshift estimation performance
KNN imputation gives highest photo-z accuracy under ideal random missingness with complete training data, while SAITS is more robust for incomplete training sets and realistic mixed missingness patterns in CSST data.
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AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
AutoGluon-Tabular achieves superior accuracy on tabular classification and regression by multi-layer model ensembling and stacking, outperforming other AutoML frameworks on 50 benchmarks and Kaggle competitions.
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Mind the Gap? A Distributional Comparison of Real and Synthetic Priors for Tabular Foundation Models
The synthetic prior for tabular foundation models covers only a narrow part of real table distributions, but this mismatch does not degrade model generalization.
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A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Pith review generated a malformed one-line summary.
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A Value-added Physical Properties Catalog for Low-redshift Galaxies from DESI Legacy Imaging Surveys DR10
A multimodal neural network trained on MPA-JHU references produces SFR, stellar mass, and metallicity estimates for 547 million low-redshift galaxies in DESI LS DR10.
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Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings
Machine learning models trained on Bangladeshi community data achieve 89-90% balanced accuracy for early CKD detection using few accessible features, outperforming traditional screening tools and generalizing across external datasets from India, UAE, and Bangladesh.
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Search for quasar pairs with Gaia astrometric data. II. Photometric redshift prediction with machine learning for the MGQPC catalogue
Machine learning models achieve NMAD 0.036 and 5.6% outliers for quasar photometric redshifts, identifying 185 high-probability pair candidates in MGQPC with 20 spectroscopically confirmed as physical pairs.
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Donor-Aware scRNA-seq Benchmarks for IBD Classification
Donor-aware benchmarks show AUROCs up to 0.978 for IBD classification from scRNA-seq using CLR cell-type compositions and GatedStructuralCFN embeddings, with compartment stratification improving both performance and feature stability.
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From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes
Hybrid predictive modeling of crash data identifies non-use of restraints as the primary risk factor for severe injury in collisions involving trees.
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Fashion Retail: Forecasting Demand for New Items
Generalized ML models trained on past sales data forecast demand for new fashion items from their attributes, with experiments across neural architectures and loss functions showing robust performance.