TabPrep is a new feature engineering pipeline that targets three data patterns and improves performance of tree-based, neural, linear, and foundation models on tabular benchmarks, often more than model architecture changes.
Climb: Class-imbalanced learning benchmark on tabular data,
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TILBench benchmark finds that no single imbalanced learning method dominates across tabular datasets; effectiveness depends on data characteristics and computational constraints.
An automated AEB annotation framework uses data augmentation and noise suppression to achieve 80% recall improvement and 50% workload reduction for rare delayed/false triggers under class imbalance and asymmetric label noise.
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
citing papers explorer
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TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
TabPrep is a new feature engineering pipeline that targets three data patterns and improves performance of tree-based, neural, linear, and foundation models on tabular benchmarks, often more than model architecture changes.
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TILBench: A Systematic Benchmark for Tabular Imbalanced Learning Across Data Regimes
TILBench benchmark finds that no single imbalanced learning method dominates across tabular datasets; effectiveness depends on data characteristics and computational constraints.
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Learning to Annotate Delayed and False AEB Events: A Practical System for Extreme Class Imbalance and Asymmetric Label Noise
An automated AEB annotation framework uses data augmentation and noise suppression to achieve 80% recall improvement and 50% workload reduction for rare delayed/false triggers under class imbalance and asymmetric label noise.
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Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.