Schema-1 is the first Data Language Model that natively understands raw tabular data and outperforms gradient-boosted ensembles, AutoML, and prior tabular foundation models on row-level prediction and imputation tasks.
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A new listwise learning-to-rank method uses smooth rank approximation and boosting to optimize without depending on a single metric.
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
XGBoost classifier filters interlopers in CSST slitless spectroscopy simulations, retaining 42% of galaxies with 96.6% accurate redshifts and 0.13% outliers.
ProfiliTable is a multi-agent system with profiler, generator, and evaluator components that outperforms baselines on 18 tabular task types via dynamic profiling and closed-loop refinement.
A gradient boosted classifier on X-ray light curve features detects stellar flares at 97.1% test accuracy and generates the largest public catalog of such events.
A data-centric AI framework cleans FLIm labels via confident learning and achieves 96% accuracy classifying glioma infiltration into low, moderate, and high cellularity.
Standalone tree-based models outperform both SAINT and SAINT-embedding hybrids for employee attrition prediction on tabular HR data.
citing papers explorer
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Data Language Models: A New Foundation Model Class for Tabular Data
Schema-1 is the first Data Language Model that natively understands raw tabular data and outperforms gradient-boosted ensembles, AutoML, and prior tabular foundation models on row-level prediction and imputation tasks.
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Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation
A new listwise learning-to-rank method uses smooth rank approximation and boosting to optimize without depending on a single metric.
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From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
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Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy
XGBoost classifier filters interlopers in CSST slitless spectroscopy simulations, retaining 42% of galaxies with 96.6% accurate redshifts and 0.13% outliers.
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ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows
ProfiliTable is a multi-agent system with profiler, generator, and evaluator components that outperforms baselines on 18 tabular task types via dynamic profiling and closed-loop refinement.
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Stellar flare detection in XMM-Newton with gradient boosted trees
A gradient boosted classifier on X-ray light curve features detects stellar flares at 97.1% test accuracy and generates the largest public catalog of such events.
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A Data-Centric Framework for Intraoperative Fluorescence Lifetime Imaging for Glioma Surgical Guidance
A data-centric AI framework cleans FLIm labels via confident learning and achieves 96% accuracy classifying glioma infiltration into low, moderate, and high cellularity.
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Integrating SAINT with Tree-Based Models: A Case Study in Employee Attrition Prediction
Standalone tree-based models outperform both SAINT and SAINT-embedding hybrids for employee attrition prediction on tabular HR data.